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paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id float64	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference stringlengths 12 47 ⌀	conference_url_abs stringlengths 16 198 ⌀	conference_url_pdf stringlengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
ee41f843-eac7-4cb1-82bf-79eb60df3c0b	cma-clip-cross-modality-attention-clip-for	2112.03562	null	https://arxiv.org/abs/2112.03562v2	https://arxiv.org/pdf/2112.03562v2.pdf	CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification	Modern Web systems such as social media and e-commerce contain rich contents expressed in images and text. Leveraging information from multi-modalities can improve the performance of machine learning tasks such as classification and recommendation. In this paper, we propose the Cross-Modality Attention Contrastive Language-Image Pre-training (CMA-CLIP), a new framework which unifies two types of cross-modality attentions, sequence-wise attention and modality-wise attention, to effectively fuse information from image and text pairs. The sequence-wise attention enables the framework to capture the fine-grained relationship between image patches and text tokens, while the modality-wise attention weighs each modality by its relevance to the downstream tasks. In addition, by adding task specific modality-wise attentions and multilayer perceptrons, our proposed framework is capable of performing multi-task classification with multi-modalities. We conduct experiments on a Major Retail Website Product Attribute (MRWPA) dataset and two public datasets, Food101 and Fashion-Gen. The results show that CMA-CLIP outperforms the pre-trained and fine-tuned CLIP by an average of 11.9% in recall at the same level of precision on the MRWPA dataset for multi-task classification. It also surpasses the state-of-the-art method on Fashion-Gen Dataset by 5.5% in accuracy and achieves competitive performance on Food101 Dataset. Through detailed ablation studies, we further demonstrate the effectiveness of both cross-modality attention modules and our method's robustness against noise in image and text inputs, which is a common challenge in practice.	['Yi Sun', 'Bryan Wang', 'Chien-Chih Wang', 'Ning Xie', 'Yang Liu', 'Jinmiao Fu', 'Shaoyuan Xu', 'Huidong Liu']	2021-12-07	null	null	null	null	['multimodal-text-and-image-classification', 'image-text-classification']	['methodology', 'miscellaneous']	[ 4.25802708e-01 -4.85083073e-01 -2.57390022e-01 -4.60600793e-01 -1.13412392e+00 -5.26302636e-01 6.98123097e-01 1.70900881e-01 -5.13890088e-01 2.72551686e-01 3.45399499e-01 -1.52980253e-01 7.14032724e-02 -5.78713894e-01 -1.11138761e+00 -6.27373695e-01 3.09352368e-01 -8.12126882e-03 -9.31295380e-02 -2.12037936e-01 -1.62762687e-01 -1.77087843e-01 -1.48728144e+00 1.02002442e+00 5.81698000e-01 1.41942143e+00 3.98394465e-01 5.51983356e-01 -1.54300049e-01 6.85829580e-01 2.99677346e-02 -7.10933745e-01 1.13139957e-01 -1.24877580e-01 -7.09348977e-01 2.40514144e-01 5.94576180e-01 -2.11182848e-01 -2.38095462e-01 9.16709900e-01 4.42657471e-01 3.07885967e-02 5.14553547e-01 -1.17414689e+00 -1.31519055e+00 6.47218049e-01 -1.10570669e+00 1.27375826e-01 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d4bb1cd3-0a43-45af-8625-0799a4467eac	facefusion-exploiting-full-spectrum-of	2305.14601	null	https://arxiv.org/abs/2305.14601v1	https://arxiv.org/pdf/2305.14601v1.pdf	FaceFusion: Exploiting Full Spectrum of Multiple Datasets	The size of training dataset is known to be among the most dominating aspects of training high-performance face recognition embedding model. Building a large dataset from scratch could be cumbersome and time-intensive, while combining multiple already-built datasets poses the risk of introducing large amount of label noise. We present a novel training method, named FaceFusion. It creates a fused view of different datasets that is untainted by identity conflicts, while concurrently training an embedding network using the view in an end-to-end fashion. Using the unified view of combined datasets enables the embedding network to be trained against the entire spectrum of the datasets, leading to a noticeable performance boost. Extensive experiments confirm superiority of our method, whose performance in public evaluation datasets surpasses not only that of using a single training dataset, but also that of previously known methods under various training circumstances.	['Dongjae Lee', 'Chiyoung Song']	2023-05-24	null	null	null	null	['face-recognition']	['computer-vision']	[ 1.78065330e-01 3.54493968e-02 -4.44437489e-02 -4.36369896e-01 -5.69340646e-01 -6.49979234e-01 6.57484531e-01 -3.17841709e-01 -3.28659981e-01 6.46873057e-01 6.04464747e-02 1.48077104e-02 -1.28329664e-01 -6.99345231e-01 -4.87792522e-01 -7.97134042e-01 1.14487462e-01 4.47925121e-01 -2.07156494e-01 6.80444464e-02 -2.76147008e-01 5.39244056e-01 -1.60538840e+00 2.08446190e-01 6.16260767e-01 1.22445858e+00 -1.52935594e-01 1.42126918e-01 -2.92499885e-02 5.61632335e-01 -5.52211046e-01 -1.01293194e+00 6.18541241e-01 -6.43665940e-02 -6.03709817e-01 1.94205537e-01 8.47312570e-01 -3.30512881e-01 -3.77918452e-01 1.07761431e+00 7.20922172e-01 -2.30341688e-01 2.51849860e-01 -1.44814479e+00 -6.20110333e-01 3.34585786e-01 -4.38776612e-01 9.38913878e-03 1.19187616e-01 -3.83714624e-02 7.17401803e-01 -1.06923556e+00 6.43869519e-01 1.10974765e+00 6.80471599e-01 6.73862517e-01 -1.38946426e+00 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7.96673834e-01 4.34018016e-01 -9.18156564e-01 -2.82830805e-01 -6.11934699e-02]	[13.139633178710938, 0.699047327041626]
7c108976-be1f-4e72-9b69-8bc1b1343ad3	salprop-salient-object-proposals-via	1706.04472	null	http://arxiv.org/abs/1706.04472v1	http://arxiv.org/pdf/1706.04472v1.pdf	SalProp: Salient object proposals via aggregated edge cues	In this paper, we propose a novel object proposal generation scheme by formulating a graph-based salient edge classification framework that utilizes the edge context. In the proposed method, we construct a Bayesian probabilistic edge map to assign a saliency value to the edgelets by exploiting low level edge features. A Conditional Random Field is then learned to effectively combine these features for edge classification with object/non-object label. We propose an objectness score for the generated windows by analyzing the salient edge density inside the bounding box. Extensive experiments on PASCAL VOC 2007 dataset demonstrate that the proposed method gives competitive performance against 10 popular generic object detection techniques while using fewer number of proposals.	['Brejesh lall', 'Prerana Mukherjee', 'Sarvaswa Tandon']	2017-06-14	null	null	null	null	['object-proposal-generation']	['computer-vision']	[ 1.10247605e-01 1.20358564e-01 -4.40553427e-01 -4.17337000e-01 -5.15268564e-01 -1.08147949e-01 4.96554732e-01 3.56915593e-01 -3.25017065e-01 5.51495075e-01 1.51152879e-01 -6.40954822e-03 -2.28195731e-02 -8.71452034e-01 -5.79656422e-01 -4.93769586e-01 -9.82603207e-02 -9.71796736e-02 1.09254444e+00 2.29712576e-01 5.56637883e-01 3.63079548e-01 -1.66274941e+00 2.32058391e-01 7.15417504e-01 1.31407773e+00 5.75440645e-01 6.41727328e-01 1.25051048e-02 6.39404953e-01 -2.38256305e-01 -1.69701740e-01 2.48711318e-01 -6.54554814e-02 -4.60769355e-01 2.82393157e-01 5.66875577e-01 -2.33029872e-01 2.14436352e-02 1.18896186e+00 3.69642496e-01 3.61907840e-01 9.13062632e-01 -1.33563340e+00 -4.35430199e-01 2.58790135e-01 -1.02497184e+00 6.78003073e-01 2.67591834e-01 -1.90354764e-01 1.28428435e+00 -1.37223363e+00 7.37399459e-01 1.04518390e+00 6.44087732e-01 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fc7ba5b6-88f7-4183-bb76-c69f1f43db42	point-to-the-expression-solving-algebraic	null	null	https://aclanthology.org/2020.emnlp-main.308	https://aclanthology.org/2020.emnlp-main.308.pdf	Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model	Solving algebraic word problems has recently emerged as an important natural language processing task. To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using {`}Op (operator/operand){'} tokens as a unit of input/output. However, such a neural model suffered two issues: expression fragmentation and operand-context separation. To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) {`}Expression{'} token and (2) operand-context pointers when generating solution equations. The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS. Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3{\%} on ALG514, 59.5{\%} on DRAW-1K, and 84.5{\%} on MAWPS. The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operand-context separation. (2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40{\%}.	['Gahgene Gweon', 'Donggeon Lee', 'Kyung Seo Ki', 'Bugeun Kim']	null	null	null	null	emnlp-2020-11	['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'reasoning', 'time-series']	[ 1.44872189e-01 -1.26554072e-01 -1.89350024e-01 -1.78322718e-01 -5.22490799e-01 -5.55572271e-01 4.18281615e-01 1.41663879e-01 -6.76009119e-01 6.50224686e-01 -2.62162000e-01 -7.76214123e-01 -1.42176196e-01 -1.26795244e+00 -5.42003572e-01 -3.05392772e-01 5.88422315e-03 8.84104744e-02 7.81394690e-02 -3.93450677e-01 4.71414447e-01 3.88871104e-01 -1.73922849e+00 5.75147867e-01 9.82302606e-01 1.32290983e+00 6.08682632e-02 4.50773269e-01 -8.88986468e-01 8.61421764e-01 -8.65544796e-01 -6.38884187e-01 3.00511241e-01 -1.57294169e-01 -8.51285279e-01 -6.89269960e-01 2.55672544e-01 -1.87344953e-01 -1.50913060e-01 1.20094097e+00 4.61956561e-01 5.26896343e-02 6.95819855e-01 -1.39665377e+00 -1.04016972e+00 1.08048570e+00 -4.88848984e-01 1.52279124e-01 3.76325339e-01 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2fafb0e4-f938-4f5d-9f86-9336f62345a4	aphmm-accelerating-profile-hidden-markov	2207.09765	null	https://arxiv.org/abs/2207.09765v1	https://arxiv.org/pdf/2207.09765v1.pdf	ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis	Profile hidden Markov models (pHMMs) are widely used in many bioinformatics applications to accurately identify similarities between biological sequences (e.g., DNA or protein sequences). PHMMs use a commonly-adopted and highly-accurate method, called the Baum-Welch algorithm, to calculate these similarities. However, the Baum-Welch algorithm is computationally expensive, and existing works provide either software- or hardware-only solutions for a fixed pHMM design. When we analyze the state-of-the-art works, we find that there is a pressing need for a flexible, high-performant, and energy-efficient hardware-software co-design to efficiently and effectively solve all the major inefficiencies in the Baum-Welch algorithm for pHMMs. We propose ApHMM, the first flexible acceleration framework that can significantly reduce computational and energy overheads of the Baum-Welch algorithm for pHMMs. ApHMM leverages hardware-software co-design to solve the major inefficiencies in the Baum-Welch algorithm by 1) designing a flexible hardware to support different pHMMs designs, 2) exploiting the predictable data dependency pattern in an on-chip memory with memoization techniques, 3) quickly eliminating negligible computations with a hardware-based filter, and 4) minimizing the redundant computations. We implement our 1) hardware-software optimizations on a specialized hardware and 2) software optimizations for GPUs to provide the first flexible Baum-Welch accelerator for pHMMs. ApHMM provides significant speedups of 15.55x-260.03x, 1.83x-5.34x, and 27.97x compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms the state-of-the-art CPU implementations of three important bioinformatics applications, 1) error correction, 2) protein family search, and 3) multiple sequence alignment, by 1.29x-59.94x, 1.03x-1.75x, and 1.03x-1.95x, respectively.	['Onur Mutlu', 'Sreenivas Subramoney', 'Juan Gómez Luna', 'Mohammed Alser', 'Joel Lindegger', 'Meryem Banu Cavlak', 'Taha Shahroodi', 'Jeremie Kim', 'Damla Senol Cali', 'Bharathwaj Suresh', 'Gurpreet S. Kalsi', 'Kamlesh Pillai', 'Can Firtina']	2022-07-20	null	null	null	null	['multiple-sequence-alignment']	['medical']	[ 1.18412092e-01 -6.26095235e-01 -1.22802034e-01 -8.79127234e-02 -8.61328840e-01 -2.59420693e-01 1.46003798e-01 4.22193408e-01 -4.01103228e-01 4.63610888e-01 -7.55342692e-02 -7.94968665e-01 3.43538165e-01 -6.04117990e-01 -6.39635921e-01 -9.41905677e-01 1.13791950e-01 4.25771385e-01 3.94583166e-01 9.23165381e-02 5.07251740e-01 4.41946417e-01 -1.49394441e+00 3.28549087e-01 6.14165187e-01 6.70438111e-01 3.02591741e-01 9.26668048e-01 -3.43262181e-02 3.70383531e-01 -2.20572263e-01 9.53167304e-02 3.47959474e-02 -5.60788035e-01 -7.98129678e-01 -3.30124557e-01 -2.32867435e-01 -2.80468225e-01 4.79372218e-02 6.04214013e-01 6.08689666e-01 -1.14515088e-02 4.82153267e-01 -1.04928946e+00 -2.30705161e-02 1.30728185e-01 -9.84845221e-01 2.03423090e-02 4.40717012e-01 4.58577722e-01 5.13144374e-01 -8.74144852e-01 5.01531184e-01 1.03770649e+00 9.83998358e-01 2.88631558e-01 -1.12845540e+00 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-3.56280208e-01 4.31745686e-03 -9.50196162e-02 -4.17482436e-01 4.76501286e-02 -8.48317593e-02]	[8.354012489318848, 3.254936695098877]
3be7b102-dc25-4b60-994d-86bf6de79c07	learning-transferable-features-for-speech	1912.11547	null	https://arxiv.org/abs/1912.11547v1	https://arxiv.org/pdf/1912.11547v1.pdf	Learning Transferable Features for Speech Emotion Recognition	Emotion recognition from speech is one of the key steps towards emotional intelligence in advanced human-machine interaction. Identifying emotions in human speech requires learning features that are robust and discriminative across diverse domains that differ in terms of language, spontaneity of speech, recording conditions, and types of emotions. This corresponds to a learning scenario in which the joint distributions of features and labels may change substantially across domains. In this paper, we propose a deep architecture that jointly exploits a convolutional network for extracting domain-shared features and a long short-term memory network for classifying emotions using domain-specific features. We use transferable features to enable model adaptation from multiple source domains, given the sparseness of speech emotion data and the fact that target domains are short of labeled data. A comprehensive cross-corpora experiment with diverse speech emotion domains reveals that transferable features provide gains ranging from 4.3% to 18.4% in speech emotion recognition. We evaluate several domain adaptation approaches, and we perform an ablation study to understand which source domains add the most to the overall recognition effectiveness for a given target domain.	['Nívio Ziviani', 'Alison Marczewski', 'Adriano Veloso']	2019-12-23	null	null	null	null	['emotional-intelligence']	['natural-language-processing']	[ 1.76492602e-01 -8.68400410e-02 -4.75800000e-02 -9.54987824e-01 -8.16575825e-01 -7.83781111e-01 4.90641296e-01 -2.17457250e-01 -3.20488751e-01 6.30348980e-01 3.92390460e-01 1.34308398e-01 1.83728337e-01 -2.05760226e-01 -3.30934405e-01 -2.69868493e-01 -9.94054079e-02 2.83833951e-01 -4.36269492e-01 -4.40827250e-01 -3.24517488e-01 4.93000984e-01 -1.36284447e+00 6.88939929e-01 6.91446483e-01 1.34010243e+00 -1.06027588e-01 8.80063176e-01 -2.86103159e-01 8.06110203e-01 -1.05472624e+00 -3.01764280e-01 -1.29365832e-01 -5.75796723e-01 -9.29567933e-01 1.58321619e-01 6.26898408e-02 -1.84736460e-01 -5.77496350e-01 7.00668216e-01 7.08756208e-01 3.22291046e-01 8.28724027e-01 -1.28226578e+00 -7.57798076e-01 2.70858437e-01 7.00392276e-02 2.03789845e-01 4.21452224e-01 -6.15652911e-02 7.74321258e-01 -7.64461696e-01 5.70772707e-01 1.07192576e+00 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f4992cd5-e2ba-4932-8976-a7eed423adb8	diachronic-word-embeddings-and-semantic	1806.03537	null	http://arxiv.org/abs/1806.03537v2	http://arxiv.org/pdf/1806.03537v2.pdf	Diachronic word embeddings and semantic shifts: a survey	Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.	['Lilja Øvrelid', 'Andrey Kutuzov', 'Terrence Szymanski', 'Erik Velldal']	2018-06-09	diachronic-word-embeddings-and-semantic-1	https://aclanthology.org/C18-1117	https://aclanthology.org/C18-1117.pdf	coling-2018-8	['diachronic-word-embeddings']	['natural-language-processing']	[ 3.19450093e-03 -7.45170265e-02 -6.39882088e-01 -1.71676025e-01 -2.62528956e-01 -7.78421998e-01 9.11194384e-01 8.90890539e-01 -8.66019726e-01 3.62415642e-01 8.24483454e-01 -4.52332616e-01 -3.64744067e-01 -7.89872110e-01 1.18045703e-01 -2.64881015e-01 -2.26648167e-01 3.13045919e-01 1.39482528e-01 -6.22321129e-01 6.87158942e-01 2.40389928e-01 -1.53291202e+00 6.49348795e-02 5.90625584e-01 4.16832596e-01 -1.92951765e-02 3.61290753e-01 -1.00049973e+00 1.81135491e-01 -5.39010823e-01 -5.97219348e-01 -1.27847314e-01 -4.13769484e-02 -9.13250685e-01 -5.64425230e-01 3.78646642e-01 2.63788342e-01 -6.25084102e-01 1.27612758e+00 6.13204002e-01 3.77889574e-01 6.75289512e-01 -1.06121445e+00 -1.53853905e+00 7.73534060e-01 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-1.35502920e-01 3.20188642e-01 4.97925758e-01 7.08347499e-01 3.71996671e-01 3.85448247e-01 8.51711184e-02 -3.21431577e-01 -4.85376358e-01 -1.07660103e+00 -6.66667998e-01 4.34207976e-01 -7.09961131e-02 -9.13798034e-01 -5.53806007e-01 -1.31964937e-01]	[10.244091033935547, 8.891861915588379]
e82746cd-e465-4f30-8d1a-d68d3a2cdbff	towards-enriched-controllability-for	2306.14917	null	https://arxiv.org/abs/2306.14917v1	https://arxiv.org/pdf/2306.14917v1.pdf	Towards Enriched Controllability for Educational Question Generation	Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit wh-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question's narrative element. The code is publicly available at github.com/bernardoleite/question-generation-control.	['Henrique Lopes Cardoso', 'Bernardo Leite']	2023-06-21	null	null	null	null	['question-generation']	['natural-language-processing']	[ 2.21885532e-01 9.76444364e-01 1.35227274e-02 -2.78671503e-01 -8.99276733e-01 -8.98885250e-01 9.35316741e-01 6.36882663e-01 -2.22240146e-02 9.45632875e-01 1.14710128e+00 -4.79300320e-01 -4.13011491e-01 -1.27565348e+00 -6.26389086e-01 3.34847271e-02 4.12456185e-01 4.70127225e-01 1.84848577e-01 -4.86924142e-01 5.10918319e-01 8.94614831e-02 -1.56546700e+00 5.15496969e-01 1.43442678e+00 2.86456347e-01 2.80105531e-01 9.92961884e-01 -5.22374213e-01 1.41052639e+00 -7.94886410e-01 -7.45143473e-01 -3.66693228e-01 -1.12212038e+00 -1.14097917e+00 -8.56022313e-02 2.67604500e-01 -7.66129047e-02 -1.33271972e-02 7.53980756e-01 4.48170096e-01 3.93176675e-01 6.10482991e-01 -1.22788835e+00 -8.58308256e-01 9.85856712e-01 4.92843956e-01 1.46909997e-01 1.09143019e+00 3.51970255e-01 1.31164634e+00 -5.94118595e-01 9.73758101e-01 1.29736388e+00 1.92055300e-01 8.73748779e-01 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-2.22923994e-01 2.26813421e-01 3.00545633e-01 -3.15135002e-01 -1.92018673e-01 1.31992459e-01]	[11.542010307312012, 8.119457244873047]
48d01c93-ea8c-45e7-9d06-e65a52ad04ed	bilingual-low-resource-neural-machine	null	null	https://aclanthology.org/R19-1003	https://aclanthology.org/R19-1003.pdf	Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish	The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines.	['Bonnie Dorr', 'Benyamin Ahmadnia']	2019-09-01	null	null	null	ranlp-2019-9	['low-resource-neural-machine-translation']	['natural-language-processing']	[-2.29125172e-01 -2.57032871e-01 -7.44150400e-01 -3.08650821e-01 -1.44732869e+00 -8.17809582e-01 7.83947408e-01 -4.26431477e-01 -8.17957580e-01 1.37511420e+00 5.13209939e-01 -8.60938609e-01 4.23299134e-01 -4.40710902e-01 -9.23587799e-01 -1.48640066e-01 5.44822752e-01 9.39611673e-01 -6.06933713e-01 -7.06400394e-01 -1.03079684e-01 2.78550386e-01 -8.30562651e-01 4.14609998e-01 1.13783193e+00 6.87452331e-02 3.63860995e-01 1.71662569e-01 -6.72218576e-02 2.60452777e-01 -5.02379894e-01 -7.16696560e-01 6.95485353e-01 -6.22776628e-01 -8.11232090e-01 -3.40395749e-01 4.29298341e-01 -2.52535075e-01 -2.60386407e-01 8.89022231e-01 7.85929680e-01 -3.47107232e-01 3.35461646e-01 -8.96896482e-01 -9.55922127e-01 1.04067731e+00 -2.76160538e-01 5.24874330e-01 1.49399891e-01 2.16639578e-01 1.12838840e+00 -1.46928930e+00 9.10513580e-01 1.13254762e+00 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cd02087e-042a-4914-933c-2d98d21a6dd8	styletts-2-towards-human-level-text-to-speech	2306.07691	null	https://arxiv.org/abs/2306.07691v1	https://arxiv.org/pdf/2306.07691v1.pdf	StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models	In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to generate the most suitable style for the text without requiring reference speech, achieving efficient latent diffusion while benefiting from the diverse speech synthesis offered by diffusion models. Furthermore, we employ large pre-trained SLMs, such as WavLM, as discriminators with our novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. Moreover, when trained on the LibriTTS dataset, our model outperforms previous publicly available models for zero-shot speaker adaptation. This work achieves the first human-level TTS on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs. The audio demos and source code are available at https://styletts2.github.io/.	['Nima Mesgarani', 'Gavin Mischler', 'Vinay S. Raghavan', 'Cong Han', 'Yinghao Aaron Li']	2023-06-13	null	null	null	null	['speech-synthesis']	['speech']	[ 1.27011940e-01 2.49452099e-01 -1.39186904e-01 -3.73860598e-01 -1.28272402e+00 -9.65476751e-01 6.07653916e-01 -6.26089990e-01 -2.81887829e-01 4.07935083e-01 5.85815191e-01 -6.26974702e-01 5.05068004e-01 -1.78608626e-01 -4.69193280e-01 -4.91748810e-01 4.49827433e-01 5.33996522e-01 5.44124562e-03 -3.64232928e-01 -4.19930786e-01 2.58182198e-01 -1.01497960e+00 3.58384162e-01 8.90155554e-01 9.33300495e-01 2.39169836e-01 1.18733370e+00 -1.17755853e-01 7.82582402e-01 -9.64432895e-01 -6.02002442e-01 2.07485944e-01 -7.90793896e-01 -4.61721987e-01 -7.73107708e-02 6.13047123e-01 -4.14813280e-01 -6.84355199e-01 5.88094532e-01 9.90364611e-01 3.42603356e-01 8.05824935e-01 -1.09256148e+00 -1.05048156e+00 9.41452384e-01 6.98693916e-02 3.86598893e-02 2.84126580e-01 5.55254877e-01 1.02911305e+00 -9.34214115e-01 4.62579101e-01 1.67084849e+00 4.83983904e-01 1.07231855e+00 -1.33288670e+00 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aeb9b158-b6d0-4ece-8933-8cbb6ebdf4d9	live-detection-of-face-using-machine-learning	null	null	https://link.springer.com/article/10.1007/s11277-018-5913-0	https://doi.org/10.1007/s11277-018-5913-0	Live Detection of Face Using Machine Learning with Multi-feature Method	Facial expression detection (FED) and extraction show the most important role in face recognition. This research proposed a new algorithm for automatic live FED using radial basis function; Haar discrete wavelet transform and Gray-level difference method is used for feature extraction and classification. Detect edges of the facial image by Otsu algorithm. The implementation results worked on Japanese Female Facial Expressions and Cohn–Kanade Extended (CK+) database for facial expression. The other database used for face detection process, namely, CMU, BioID, Long Distance, and FEI. It is usually possible for practical recognition system to record (by a camera or by computer) multiple face images from each subject. Choosing face images with high tone for recognition is a promising strategy for improving the system performance. We propose a learning to rank based (solid basic structure on which bigger things can be built) for evaluating the face image quality. But we improved limitations of this algorithm using contrast enhancement. We solved the problem of long distance and low contrast images. In the initial preprocess stage; perform median filtering for removing noise from an image. This step enhances the feature extraction process. Finding an image from the image components is a typical task in pattern recognition. The detection rate has reached up to 100% for expression recognition. The proposed system estimates the value of precision and recall. This algorithm is compared with the previous algorithm and our proposed proved better than previous algorithms.	['Jagdish Kumar', 'Sukhwinder Singh', 'Sandeep Kumar']	2018-07-27	null	null	null	null	['face-image-quality']	['computer-vision']	[ 1.75290376e-01 -4.02721554e-01 -9.03494284e-02 -5.92380524e-01 -4.44995493e-01 -1.18262038e-01 1.18896820e-01 -6.11193299e-01 -7.16806591e-01 8.08350325e-01 1.62592605e-02 2.05690116e-01 -2.22582355e-01 -7.97615290e-01 -1.18233198e-02 -1.03235865e+00 -1.28819361e-01 1.94374904e-01 -1.30553037e-01 -2.80190438e-01 4.34957474e-01 1.23762596e+00 -1.96958935e+00 4.42242980e-01 3.29068929e-01 1.10908020e+00 1.68768570e-01 7.79222250e-01 -1.97550893e-01 1.02714598e+00 -6.25001729e-01 -2.58890301e-01 1.17364600e-01 -7.73381114e-01 -7.39770293e-01 4.71044660e-01 6.14576451e-02 -3.72576803e-01 2.63871878e-01 1.27946770e+00 8.34277511e-01 1.12161376e-02 9.80384529e-01 -1.35506260e+00 -5.59182465e-01 -7.56366104e-02 -1.07982540e+00 5.05628407e-01 3.82325977e-01 -2.20576569e-01 5.58679365e-02 -1.38710451e+00 6.26205623e-01 1.26286733e+00 6.09735191e-01 6.99703276e-01 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33c71481-b7d1-4538-96df-28af52c3f816	multi-agent-reinforcement-learning-for-14	2211.16385	null	https://arxiv.org/abs/2211.16385v1	https://arxiv.org/pdf/2211.16385v1.pdf	Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration	Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fine-tuning architectural parameters across multiple sub-systems (e.g., datapath, memory blocks in different hierarchies, interconnects, compiler optimization, etc.) quickly results in a combinatorial explosion of design space. This makes domain-specific customization an extremely challenging task. Prior work explores using reinforcement learning (RL) and other optimization methods to automatically explore the large design space. However, these methods have traditionally relied on single-agent RL/ML formulations. It is unclear how scalable single-agent formulations are as we increase the complexity of the design space (e.g., full stack System-on-Chip design). Therefore, we propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. The key idea behind using MARL is an observation that parameters across different sub-systems are more or less independent, thus allowing a decentralized role assigned to each agent. We test this hypothesis by designing domain-specific DRAM memory controller for several workload traces. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines such as Proximal Policy Optimization and Soft Actor-Critic over different target objectives such as low power and latency. To this end, this work opens the pathway for new and promising research in MARL solutions for hardware architecture search.	['Aleksandra Faust', 'Vijay Janapa Reddi', 'Izzeddin Gur', 'Dan Zhang', 'Shayegan Omidshafiei', 'Natasha Jaques', 'Srivatsan Krishnan']	2022-11-29	null	null	null	null	['compiler-optimization']	['computer-code']	[-3.78337204e-01 -2.28761405e-01 -7.04017460e-01 -2.35590152e-02 -8.99758160e-01 -6.23766780e-01 3.21370959e-01 1.64115623e-01 -3.00703675e-01 9.91284728e-01 1.47910163e-01 -5.58296561e-01 -1.23824224e-01 -7.37876892e-01 -6.05584443e-01 -8.51663530e-01 2.26436798e-02 6.34276390e-01 1.73656851e-01 -3.33183944e-01 4.51913446e-01 5.78036010e-01 -1.12325609e+00 -8.01936910e-02 5.10318041e-01 6.70072198e-01 1.87874399e-02 5.18784225e-01 -5.86187001e-04 5.19523621e-01 -6.15566373e-01 1.97030708e-01 2.02238277e-01 -4.33533996e-01 -5.46941996e-01 -9.48005989e-02 -9.92885530e-02 -2.13140115e-01 3.95326316e-02 8.85600209e-01 6.52442575e-01 -2.76430119e-02 3.98054123e-01 -1.16042936e+00 5.14999479e-02 1.06223345e+00 -9.07427132e-01 1.36804089e-01 -1.19304232e-01 5.39175928e-01 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7.71161914e-01 1.21594477e+00 7.25907326e-01 -9.49984074e-01 -7.63229728e-01 -1.15383826e-01 -3.08361381e-01 -5.95431268e-01 3.40465903e-01 -7.97901809e-01 1.13475785e-01 -4.39674258e-01]	[5.603866100311279, 3.1329166889190674]
0038a3be-8928-467b-8b01-181b82bacc0e	deep-flow-guided-video-inpainting	1905.02884	null	https://arxiv.org/abs/1905.02884v1	https://arxiv.org/pdf/1905.02884v1.pdf	Deep Flow-Guided Video Inpainting	Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video contents. In this work we propose a novel flow-guided video inpainting approach. Rather than filling in the RGB pixels of each frame directly, we consider video inpainting as a pixel propagation problem. We first synthesize a spatially and temporally coherent optical flow field across video frames using a newly designed Deep Flow Completion network. Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video. Specifically, the Deep Flow Completion network follows a coarse-to-fine refinement to complete the flow fields, while their quality is further improved by hard flow example mining. Following the guide of the completed flow, the missing video regions can be filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of inpainting quality and speed.	['Chen Change Loy', 'Rui Xu', 'Xiaoxiao Li', 'Bolei Zhou']	2019-05-08	deep-flow-guided-video-inpainting-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_Deep_Flow-Guided_Video_Inpainting_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Deep_Flow-Guided_Video_Inpainting_CVPR_2019_paper.pdf	cvpr-2019-6	['one-shot-visual-object-segmentation', 'video-inpainting']	['computer-vision', 'computer-vision']	[ 1.87534511e-01 -1.53869212e-01 -1.48824856e-01 -1.14249490e-01 -4.79590386e-01 -5.10050774e-01 8.69354159e-02 -2.17399895e-01 -2.96282530e-01 1.02623153e+00 3.01204175e-01 2.46271808e-02 2.03840345e-01 -6.97755516e-01 -9.08025563e-01 -4.52222943e-01 4.88014407e-02 -3.02253179e-02 2.32385963e-01 1.33003637e-01 2.59355336e-01 4.78163004e-01 -1.27726924e+00 2.10915193e-01 9.97737288e-01 1.05255210e+00 3.20649207e-01 6.88846886e-01 -2.86398560e-01 1.34275758e+00 -2.50870466e-01 -9.07453075e-02 5.21347761e-01 -6.46664202e-01 -8.17145348e-01 5.24916351e-01 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7f6bea25-9291-4b5e-8012-de213da0407f	rethinking-semi-supervised-learning-with	2305.13002	null	https://arxiv.org/abs/2305.13002v1	https://arxiv.org/pdf/2305.13002v1.pdf	Rethinking Semi-supervised Learning with Language Models	Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks. Currently, there are two popular approaches to make use of unlabelled data: Self-training (ST) and Task-adaptive pre-training (TAPT). ST uses a teacher model to assign pseudo-labels to the unlabelled data, while TAPT continues pre-training on the unlabelled data before fine-tuning. To the best of our knowledge, the effectiveness of TAPT in SSL tasks has not been systematically studied, and no previous work has directly compared TAPT and ST in terms of their ability to utilize the pool of unlabelled data. In this paper, we provide an extensive empirical study comparing five state-of-the-art ST approaches and TAPT across various NLP tasks and data sizes, including in- and out-of-domain settings. Surprisingly, we find that TAPT is a strong and more robust SSL learner, even when using just a few hundred unlabelled samples or in the presence of domain shifts, compared to more sophisticated ST approaches, and tends to bring greater improvements in SSL than in fully-supervised settings. Our further analysis demonstrates the risks of using ST approaches when the size of labelled or unlabelled data is small or when domain shifts exist. We offer a fresh perspective for future SSL research, suggesting the use of unsupervised pre-training objectives over dependency on pseudo labels.	['Yunlong Jiao', 'Gabriella Kazai', 'Emine Yilmaz', 'Nikolaos Aletras', 'Francesco Tonolini', 'Zhengxiang Shi']	2023-05-22	null	null	null	null	['unsupervised-pre-training', 'pseudo-label', 'semi-supervised-text-classification-1']	['methodology', 'miscellaneous', 'natural-language-processing']	[ 5.53041875e-01 4.83733177e-01 -5.52860081e-01 -5.85773647e-01 -1.06521547e+00 -7.34522104e-01 8.44443083e-01 4.23595935e-01 -8.62436593e-01 8.68115067e-01 2.46248141e-01 -5.35326838e-01 -1.20215967e-01 -3.20753664e-01 -6.28021657e-01 -4.51317936e-01 2.10966885e-01 8.18290114e-01 2.22781733e-01 -7.24161267e-02 7.34096691e-02 3.09473246e-01 -1.36925852e+00 3.19085538e-01 8.05621207e-01 5.65349460e-01 1.20571114e-01 4.28861260e-01 -3.02693903e-01 6.54360533e-01 -4.91719097e-01 -1.99479699e-01 3.99241328e-01 -3.10724914e-01 -9.68393207e-01 9.92899388e-02 1.99147820e-01 1.31705731e-01 8.40874240e-02 5.78461289e-01 6.01671517e-01 3.28317881e-01 6.64990604e-01 -1.05833292e+00 -4.21509504e-01 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-1.20178664e+00 1.99934512e-01 -4.24682856e-01 2.64584392e-01 3.65757763e-01 1.31955969e+00 4.01063830e-01 1.50023386e-01 3.19921583e-01 -7.11516321e-01 -6.56520069e-01 -1.32148278e+00 -4.30251032e-01 6.62890732e-01 2.29306877e-01 -6.73048079e-01 -5.80944777e-01 1.32286295e-01]	[10.715947151184082, 8.15636920928955]
d0e3f2a6-b2d7-4f63-a293-008e351b84c1	training-frankensteins-creature-to-stack	1810.11714	null	http://arxiv.org/abs/1810.11714v2	http://arxiv.org/pdf/1810.11714v2.pdf	The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints	A robot can now grasp an object more effectively than ever before, but once it has the object what happens next? We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances. To address this, we introduce the JHU CoSTAR Block Stacking Dataset (BSD), where a robot interacts with 5.1 cm colored blocks to complete an order-fulfillment style block stacking task. It contains dynamic scenes and real time-series data in a less constrained environment than comparable datasets. There are nearly 12,000 stacking attempts and over 2 million frames of real data. We discuss the ways in which this dataset provides a valuable resource for a broad range of other topics of investigation. We find that hand-designed neural networks that work on prior datasets do not generalize to this task. Thus, to establish a baseline for this dataset, we demonstrate an automated search of neural network based models using a novel multiple-input HyperTree MetaModel, and find a final model which makes reasonable 3D pose predictions for grasping and stacking on our dataset. The CoSTAR BSD, code, and instructions are available at https://sites.google.com/site/costardataset.	['Gregory D. Hager', 'Chia-Hung Lin', 'Chris Paxton', 'Andrew Hundt', 'Varun Jain']	2018-10-27	null	null	null	null	['6d-pose-estimation-using-rgbd', 'industrial-robots', 'robot-task-planning']	['computer-vision', 'robots', 'robots']	[ 2.32461050e-01 -1.32868411e-02 -1.09812669e-01 -3.79776806e-01 -4.70579177e-01 -6.88849628e-01 1.96517855e-01 -3.44173163e-01 -1.47176415e-01 4.88141805e-01 5.79245389e-02 -3.43554050e-01 -4.94319916e-01 -4.19007391e-01 -1.32808971e+00 -6.45636976e-01 -5.24538696e-01 7.45639205e-01 1.76226124e-01 -3.72969478e-01 4.54913408e-01 8.88126850e-01 -1.70033038e+00 7.64754236e-01 2.58116752e-01 8.20712984e-01 9.23796237e-01 5.18747330e-01 3.90650690e-01 5.20752847e-01 -2.58150101e-01 5.86897135e-02 7.67198980e-01 4.93692905e-01 -9.15407360e-01 -1.26263991e-01 4.86576140e-01 -8.94726574e-01 -6.22563720e-01 5.00022590e-01 3.26649696e-01 1.59267727e-02 6.26195192e-01 -1.36698699e+00 -6.98037982e-01 8.30527902e-01 -2.51453608e-01 -2.53768474e-01 1.03588440e-01 5.03811002e-01 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8581c516-c998-4ee7-b9d5-8ef41cfb6f8a	sequential-pattern-mining-in-educational-data	2302.01932	null	https://arxiv.org/abs/2302.01932v1	https://arxiv.org/pdf/2302.01932v1.pdf	Sequential pattern mining in educational data: The application context, potential, strengths, and limitations	Increasingly, researchers have suggested the benefits of temporal analysis to improve our understanding of the learning process. Sequential pattern mining (SPM), as a pattern recognition technique, has the potential to reveal the temporal aspects of learning and can be a valuable tool in educational data science. However, its potential is not well understood and exploited. This chapter addresses this gap by reviewing work that utilizes sequential pattern mining in educational contexts. We identify that SPM is suitable for mining learning behaviors, analyzing and enriching educational theories, evaluating the efficacy of instructional interventions, generating features for prediction models, and building educational recommender systems. SPM can contribute to these purposes by discovering similarities and differences in learners' activities and revealing the temporal change in learning behaviors. As a sequential analysis method, SPM can reveal unique insights about learning processes and be powerful for self-regulated learning research. It is more flexible in capturing the relative arrangement of learning events than the other sequential analysis methods. Future research may improve its utility in educational data science by developing tools for counting pattern occurrences as well as identifying and removing unreliable patterns. Future work needs to establish a systematic guideline for data preprocessing, parameter setting, and interpreting sequential patterns.	['Luc Paquette', 'Yingbin Zhang']	2023-02-03	null	null	null	null	['sequential-pattern-mining']	['natural-language-processing']	[ 9.54240784e-02 -4.14649397e-01 -8.06128502e-01 -3.14567953e-01 1.07287459e-01 -5.99226415e-01 3.35780144e-01 8.87531817e-01 -3.26143112e-03 2.76985496e-01 5.22322714e-01 -6.49492919e-01 -7.81009793e-01 -8.38140666e-01 -2.89686024e-01 -5.59331894e-01 -1.06280416e-01 -9.30435136e-02 3.54093611e-01 -1.33456783e-02 7.89935768e-01 6.97580159e-01 -2.40140605e+00 5.02094507e-01 1.10955858e+00 3.34584385e-01 4.27851886e-01 3.28611046e-01 -6.56704903e-01 9.60986137e-01 -6.25181556e-01 1.18342731e-02 -3.05418402e-01 -7.40221858e-01 -7.67394245e-01 6.92721009e-02 3.46624553e-01 2.32967865e-02 5.23767844e-02 7.71125078e-01 1.85467765e-01 5.16013384e-01 8.91874060e-02 -9.91521716e-01 -5.50910592e-01 5.56160986e-01 -2.86868304e-01 7.83002734e-01 8.87847126e-01 -1.85481548e-01 5.73027611e-01 -5.14115572e-01 4.32790786e-01 8.62890124e-01 5.10938108e-01 4.30802017e-01 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01fe9311-1298-4831-aab2-4f2c14c8c057	hierarchical-and-decentralised-federated	2304.14982	null	https://arxiv.org/abs/2304.14982v1	https://arxiv.org/pdf/2304.14982v1.pdf	Hierarchical and Decentralised Federated Learning	Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, the rise of complex cyber-physical systems, such as the Internet-of-Things, presents new challenges that are not met with traditional FL methods. Hierarchical Federated Learning extends the traditional FL process to enable more efficient model aggregation based on application needs or characteristics of the deployment environment (e.g., resource capabilities and/or network connectivity). It illustrates the benefits of balancing processing across the cloud-edge continuum. Hierarchical Federated Learning is likely to be a key enabler for a wide range of applications, such as smart farming and smart energy management, as it can improve performance and reduce costs, whilst also enabling FL workflows to be deployed in environments that are not well-suited to traditional FL. Model aggregation algorithms, software frameworks, and infrastructures will need to be designed and implemented to make such solutions accessible to researchers and engineers across a growing set of domains. H-FL also introduces a number of new challenges. For instance, there are implicit infrastructural challenges. There is also a trade-off between having generalised models and personalised models. If there exist geographical patterns for data (e.g., soil conditions in a smart farm likely are related to the geography of the region itself), then it is crucial that models used locally can consider their own locality in addition to a globally-learned model. H-FL will be crucial to future FL solutions as it can aggregate and distribute models at multiple levels to optimally serve the trade-off between locality dependence and global anomaly robustness.	['Aftab Khan', 'Ian Foster', 'Kyle Chard', 'Matt Baughman', 'Nathaniel Hudson', 'Theodoros Spyridopoulos', 'Omer Rana']	2023-04-28	null	null	null	null	['energy-management']	['time-series']	[-4.92738634e-02 -2.66244709e-01 -1.92547753e-01 -3.84936482e-01 -2.81255633e-01 -9.38958704e-01 3.33132356e-01 5.73076606e-01 -2.73495764e-02 4.66434270e-01 9.45221484e-02 -5.39330542e-01 -7.56398976e-01 -1.07488751e+00 -5.19840479e-01 -6.35527909e-01 -5.12754023e-01 5.11031687e-01 6.83216751e-02 -2.54652146e-02 -8.22094753e-02 9.89031136e-01 -1.65129912e+00 1.53119534e-01 6.91022873e-01 1.08867741e+00 1.81040704e-01 7.35401213e-01 -1.98193923e-01 5.51481307e-01 -4.16497499e-01 -1.11268900e-01 5.62130928e-01 -5.80535084e-02 -5.66358626e-01 -2.73524702e-01 1.10111028e-01 -2.07275152e-01 2.71642625e-01 7.91182756e-01 5.09648442e-01 -3.18836654e-03 5.32977097e-02 -1.48995531e+00 -2.32107401e-01 4.30966467e-01 -3.59196395e-01 -7.08680823e-02 -1.00854315e-01 5.16263902e-01 5.40143490e-01 1.39912009e-01 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f5416041-ebfb-48fe-9fb0-c7260e28da1c	nystrom-method-for-accurate-and-scalable	2302.09726	null	https://arxiv.org/abs/2302.09726v1	https://arxiv.org/pdf/2302.09726v1.pdf	Nystrom Method for Accurate and Scalable Implicit Differentiation	The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network parameters. This paper proposes to tackle this problem by the Nystrom method and the Woodbury matrix identity, exploiting the low-rankness of the Hessian. Compared to existing methods using iterative approximation, such as conjugate gradient and the Neumann series approximation, the proposed method avoids numerical instability and can be efficiently computed in matrix operations without iterations. As a result, the proposed method works stably in various tasks and is faster than iterative approximations. Throughout experiments including large-scale hyperparameter optimization and meta learning, we demonstrate that the Nystrom method consistently achieves comparable or even superior performance to other approaches. The source code is available from https://github.com/moskomule/hypergrad.	['Makoto Yamada', 'Ryuichiro Hataya']	2023-02-20	null	null	null	null	['bilevel-optimization']	['methodology']	[-4.57592934e-01 -2.24021778e-01 -2.89977044e-01 -1.34522811e-01 -7.49257863e-01 -3.70593399e-01 4.01778251e-01 -2.21675619e-01 -8.12257349e-01 9.87055242e-01 -1.60496324e-01 -3.86589140e-01 -3.88228834e-01 -1.59030646e-01 -6.18149698e-01 -9.68430400e-01 -1.35389036e-02 5.08541763e-01 -2.62174457e-01 -3.04204255e-01 5.11208177e-01 3.85187805e-01 -1.11651957e+00 -3.45750034e-01 1.00537872e+00 1.05342448e+00 -1.30319968e-01 6.11569881e-01 -5.87311126e-02 7.52010584e-01 -1.67624980e-01 -3.10887218e-01 6.50490046e-01 -2.06321537e-01 -6.23605132e-01 -1.15682453e-01 6.30535364e-01 -3.72730941e-01 -3.42330992e-01 1.19356334e+00 6.22988880e-01 5.34206986e-01 5.64350545e-01 -1.25419188e+00 -3.68224174e-01 3.84226412e-01 -5.76712489e-01 1.05119489e-01 6.74210605e-05 7.88250193e-02 1.14642239e+00 -1.64205909e+00 3.09376001e-01 1.09565568e+00 1.11094749e+00 1.67926654e-01 -1.25285470e+00 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2.72498578e-01 3.88576388e-01 -6.46774828e-01 -8.31726432e-01 -2.57956833e-01]	[6.940527439117432, 4.21420955657959]
efd807d2-c6eb-4d6d-b53e-dae62bad7a94	self-learning-with-rectification-strategy-for	2004.08055	null	https://arxiv.org/abs/2004.08055v1	https://arxiv.org/pdf/2004.08055v1.pdf	Self-Learning with Rectification Strategy for Human Parsing	In this paper, we solve the sample shortage problem in the human parsing task. We begin with the self-learning strategy, which generates pseudo-labels for unlabeled data to retrain the model. However, directly using noisy pseudo-labels will cause error amplification and accumulation. Considering the topology structure of human body, we propose a trainable graph reasoning method that establishes internal structural connections between graph nodes to correct two typical errors in the pseudo-labels, i.e., the global structural error and the local consistency error. For the global error, we first transform category-wise features into a high-level graph model with coarse-grained structural information, and then decouple the high-level graph to reconstruct the category features. The reconstructed features have a stronger ability to represent the topology structure of the human body. Enlarging the receptive field of features can effectively reducing the local error. We first project feature pixels into a local graph model to capture pixel-wise relations in a hierarchical graph manner, then reverse the relation information back to the pixels. With the global structural and local consistency modules, these errors are rectified and confident pseudo-labels are generated for retraining. Extensive experiments on the LIP and the ATR datasets demonstrate the effectiveness of our global and local rectification modules. Our method outperforms other state-of-the-art methods in supervised human parsing tasks.	['Zhiyuan Liang', 'Jianbing Shen', 'Jiahao Gong', 'Tao Li', 'Sanyuan Zhao']	2020-04-17	self-learning-with-rectification-strategy-for-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.pdf	cvpr-2020-6	['human-parsing']	['computer-vision']	[ 0.37861693 0.8524986 -0.3140355 -0.74566716 -0.5229971 -0.2433031 -0.24782343 -0.17021653 -0.00898938 0.5671691 0.23714522 0.19766113 0.09009293 -0.9431665 -0.8351458 -0.6391111 0.21616374 0.39422834 0.4028415 -0.03954855 -0.0188586 0.1615017 -1.5149848 0.35294348 1.1315161 0.9659557 0.17588964 0.39052722 -0.45067132 0.89883375 -0.40776893 -0.425372 0.1618843 -0.55226964 -1.0789226 0.5503321 0.57220834 -0.33049732 -0.34196067 1.494535 0.27157912 -0.02200383 0.35293895 -1.0414344 -1.0317702 0.6978749 -0.6915712 -0.25355884 0.37219688 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532bae58-9480-4a2f-9ae3-0070cad54663	bert-memorisation-and-pitfalls-in-low	2105.00828	null	https://arxiv.org/abs/2105.00828v2	https://arxiv.org/pdf/2105.00828v2.pdf	Memorisation versus Generalisation in Pre-trained Language Models	State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios. We find that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal results even on extremely noisy datasets. However, our experiments also show that they mainly learn from high-frequency patterns and largely fail when tested on low-resource tasks such as few-shot learning and rare entity recognition. To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks.	['Marek Rei', 'Sebastian Ruder', 'Michael Tänzer']	2021-04-16	null	https://aclanthology.org/2022.acl-long.521	https://aclanthology.org/2022.acl-long.521.pdf	acl-2022-5	['low-resource-named-entity-recognition']	['natural-language-processing']	[ 2.40512509e-02 1.11788370e-01 -1.99837133e-01 -4.06552941e-01 -5.92529416e-01 -2.64560789e-01 8.89017522e-01 3.72953534e-01 -1.03235948e+00 1.03914952e+00 4.97621685e-01 -2.29706511e-01 -9.14768055e-02 -1.04973972e+00 -6.34072304e-01 -2.08440095e-01 -2.01693207e-01 5.13457417e-01 2.98709571e-01 -2.65334249e-01 7.81714618e-02 2.36391559e-01 -1.72280741e+00 4.92519289e-01 6.80819213e-01 5.60323477e-01 1.52611807e-01 5.21501660e-01 -6.05449080e-01 1.21398449e+00 -7.17614949e-01 -5.47322869e-01 1.22317346e-02 -1.81196347e-01 -1.19369137e+00 -3.96170914e-01 3.86293411e-01 -1.03384778e-01 -4.38845366e-01 5.87604403e-01 8.82179797e-01 7.45839715e-01 4.81016278e-01 -6.23045206e-01 -9.79831874e-01 7.13007689e-01 1.12026416e-01 8.24358404e-01 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196703d9-afe3-4b9e-bcd6-8c34faf5f5b0	clubmark-a-parallel-isolation-framework-for	null	null	https://arxiv.org/abs/1902.00475	https://arxiv.org/pdf/1902.00475	Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures	There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking of diverse clustering algorithms on a wide range of synthetic and real-world datasets. In this paper, we introduce Clubmark, a new extensible framework that aims to fill this gap by providing a parallel isolation benchmarking platform for clustering algorithms and their evaluation on NUMA servers. Clubmark allows for fine-grained control over various execution variables (timeouts, memory consumption, CPU affinity and cache policy) and supports the evaluation of a wide range of clustering algorithms including multi-level, hierarchical and overlapping clustering techniques on both weighted and unweighted input networks with built-in evaluation of several extrinsic and intrinsic measures. Our framework is open-source and provides a consistent and systematic way to execute, evaluate and profile clustering techniques considering a number of aspects that are often missing in state-of-the-art frameworks and benchmarking systems.	['Philippe Cudré-Mauroux', 'Mourad Khayati', 'Artem Lutov']	2018-11-17	null	null	null	2018-ieee-international-conference-on-data	['clustering-algorithms-evaluation']	['methodology']	[-2.33456209e-01 -7.00171828e-01 6.63490966e-02 -2.81195194e-01 -3.27591628e-01 -7.74731100e-01 6.73428297e-01 6.28690541e-01 -4.76280838e-01 5.58529437e-01 -1.27646312e-01 -2.98853964e-01 -7.20179677e-01 -9.75406170e-01 6.70271590e-02 -1.01576638e+00 -5.37274718e-01 1.16262639e+00 7.11503327e-01 -2.36815494e-02 3.36916268e-01 7.87816048e-01 -2.05546117e+00 4.45988566e-01 5.14482319e-01 5.36393285e-01 1.46574840e-01 7.55228758e-01 -1.01802133e-01 1.85037643e-01 -4.45154339e-01 -1.02384932e-01 -2.99287010e-02 -2.95516104e-01 -1.00753367e+00 -1.40605733e-01 -7.96673894e-02 4.55951422e-01 1.06013633e-01 7.72047400e-01 6.86707973e-01 -6.71758726e-02 7.24636495e-01 -1.32454169e+00 3.63314338e-02 8.96934509e-01 -6.37909949e-01 3.14399332e-01 1.66096702e-01 9.65678468e-02 8.67896497e-01 -5.28174520e-01 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d6b50c7d-da8c-4ea1-a437-e0fcd39b8d72	unveiling-the-political-agenda-of-the	1505.07302	null	http://arxiv.org/abs/1505.07302v4	http://arxiv.org/pdf/1505.07302v4.pdf	Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis	This study analyzes political interactions in the European Parliament (EP) by considering how the political agenda of the plenary sessions has evolved over time and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making Parliamentary speeches. It does so by considering the context in which speeches are made, and the content of those speeches. To detect latent themes in legislative speeches over time, speech content is analyzed using a new dynamic topic modeling method, based on two layers of matrix factorization. This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999-2014. Our findings suggest that the political agenda of the EP has evolved significantly over time, is impacted upon by the committee structure of the Parliament, and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro-crisis have a significant impact on what is being discussed in Parliament.	['James P. Cross', 'Derek Greene']	2015-05-27	null	null	null	null	['dynamic-topic-modeling']	['natural-language-processing']	[-6.21477105e-02 4.85836774e-01 -4.54064049e-02 -3.14430833e-01 -5.65809786e-01 -1.01923120e+00 1.15408576e+00 6.52384758e-01 -8.57746601e-01 5.46308339e-01 1.48328006e+00 -8.51806879e-01 -1.61358178e-01 -8.10942829e-01 -4.76561189e-01 -6.35726869e-01 5.48802555e-01 2.21991062e-01 -2.46422008e-01 -4.36824769e-01 1.05121709e-01 -6.65199980e-02 -8.44061673e-01 6.69073999e-01 3.54488283e-01 3.21492963e-02 -5.06168790e-02 4.70078200e-01 -3.07297170e-01 8.02702487e-01 -9.65995550e-01 -3.33993763e-01 6.74535334e-02 -2.26003319e-01 -9.27939355e-01 2.33441100e-01 3.45840841e-03 2.45155245e-01 -6.85424566e-01 7.57381558e-01 3.44311655e-01 2.29507595e-01 4.48652565e-01 4.53653447e-02 2.49150723e-01 1.05259168e+00 -3.60104889e-01 4.25952613e-01 3.53273958e-01 -1.24750622e-01 9.93421018e-01 -4.49479222e-01 9.79812622e-01 1.60509419e+00 3.37485999e-01 2.17246369e-01 -1.36490703e+00 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-4.46608335e-01 2.64797062e-01 1.38568711e-02 -8.16228166e-02 -4.80079800e-01 -3.06097984e-01]	[8.943558692932129, 9.848655700683594]
48bf0fdf-9a0e-40ec-a987-6c4a0f029b51	t-wavenet-tree-structured-wavelet-neural	2012.05456	null	https://arxiv.org/abs/2012.05456v1	https://arxiv.org/pdf/2012.05456v1.pdf	T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis	Sensor-based time series analysis is an essential task for applications such as activity recognition and brain-computer interface. Recently, features extracted with deep neural networks (DNNs) are shown to be more effective than conventional hand-crafted ones. However, most of these solutions rely solely on the network to extract application-specific information carried in the sensor data. Motivated by the fact that usually a small subset of the frequency components carries the primary information for sensor data, we propose a novel tree-structured wavelet neural network for sensor data analysis, namely \emph{T-WaveNet}. To be specific, with T-WaveNet, we first conduct a power spectrum analysis for the sensor data and decompose the input signal into various frequency subbands accordingly. Then, we construct a tree-structured network, and each node on the tree (corresponding to a frequency subband) is built with an invertible neural network (INN) based wavelet transform. By doing so, T-WaveNet provides more effective representation for sensor information than existing DNN-based techniques, and it achieves state-of-the-art performance on various sensor datasets, including UCI-HAR for activity recognition, OPPORTUNITY for gesture recognition, BCICIV2a for intention recognition, and NinaPro DB1 for muscular movement recognition.	['Qiang Xu', 'Qiuxia Lai', 'Ailing Zeng', 'Minhao Liu']	2020-12-10	null	null	null	null	['muscular-movement-recognition']	['medical']	[ 5.06456673e-01 -3.09636593e-01 -3.06024998e-01 -2.36497670e-01 -3.55689377e-01 -2.88028326e-02 3.23909186e-02 -1.13929644e-01 -5.65382898e-01 4.85012293e-01 3.48242819e-01 -1.07789606e-01 -3.47175509e-01 -8.54890406e-01 -4.11658227e-01 -1.01105011e+00 -3.54149610e-01 -4.14106727e-01 9.73085389e-02 -2.26941079e-01 -1.55739546e-01 3.43131691e-01 -1.46415663e+00 2.82527864e-01 6.03000462e-01 1.71673214e+00 4.10264917e-02 2.84001023e-01 -6.32868633e-02 5.91704190e-01 -8.27863991e-01 2.95203388e-01 1.33414427e-02 -4.59249467e-01 -3.02824527e-01 -1.45100728e-01 -3.52312386e-01 -2.01277211e-01 -5.23321569e-01 1.04898489e+00 6.22765422e-01 1.06145427e-01 5.06641448e-01 -1.16248524e+00 -1.86026201e-01 6.14856482e-01 -3.87421131e-01 5.03956616e-01 1.85824692e-01 -1.75888091e-01 6.16703510e-01 -7.45376110e-01 2.75006384e-01 7.96330750e-01 9.07756388e-01 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61bc0879-d5f5-47e5-a83a-51fb10026211	mtp-multi-hypothesis-tracking-and-prediction	2110.09481	null	https://arxiv.org/abs/2110.09481v1	https://arxiv.org/pdf/2110.09481v1.pdf	MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation	Recently, there has been tremendous progress in developing each individual module of the standard perception-planning robot autonomy pipeline, including detection, tracking, prediction of other agents' trajectories, and ego-agent trajectory planning. Nevertheless, there has been less attention given to the principled integration of these components, particularly in terms of the characterization and mitigation of cascading errors. This paper addresses the problem of cascading errors by focusing on the coupling between the tracking and prediction modules. First, by using state-of-the-art tracking and prediction tools, we conduct a comprehensive experimental evaluation of how severely errors stemming from tracking can impact prediction performance. On the KITTI and nuScenes datasets, we find that predictions consuming tracked trajectories as inputs (the typical case in practice) can experience a significant (even order of magnitude) drop in performance in comparison to the idealized setting where ground truth past trajectories are used as inputs. To address this issue, we propose a multi-hypothesis tracking and prediction framework. Rather than relying on a single set of tracking results for prediction, our framework simultaneously reasons about multiple sets of tracking results, thereby increasing the likelihood of including accurate tracking results as inputs to prediction. We show that this framework improves overall prediction performance over the standard single-hypothesis tracking-prediction pipeline by up to 34.2% on the nuScenes dataset, with even more significant improvements (up to ~70%) when restricting the evaluation to challenging scenarios involving identity switches and fragments -- all with an acceptable computation overhead.	['Marco Pavone', 'Boris Ivanovic', 'Xinshuo Weng']	2021-10-18	null	null	null	null	['trajectory-planning']	['robots']	[ 1.26467377e-01 1.70928642e-01 -1.75177157e-01 -6.44384474e-02 -6.77211761e-01 -8.00507307e-01 1.00389254e+00 2.89468646e-01 -4.65863734e-01 5.92356324e-01 1.55793235e-01 -2.68717080e-01 -9.56233442e-02 -7.93489993e-01 -8.99875641e-01 -4.63259578e-01 -4.42859977e-01 7.65683651e-01 8.67675662e-01 -2.97971368e-01 7.79801309e-02 4.63084191e-01 -1.96357703e+00 -2.61153251e-01 6.13146365e-01 7.46263087e-01 1.42143920e-01 7.43326843e-01 4.53229964e-01 7.07751274e-01 -3.25513035e-01 -2.65008032e-01 4.09364998e-01 7.29014352e-03 -5.21903694e-01 -4.05840129e-02 5.12925327e-01 -3.73838127e-01 -3.68948162e-01 9.40126717e-01 2.49982387e-01 2.04887822e-01 2.68593609e-01 -1.56660461e+00 -4.91151735e-02 3.69902611e-01 -1.77773699e-01 1.06442124e-01 6.07344508e-01 5.31256318e-01 9.06092286e-01 -5.63006818e-01 8.85359943e-01 1.06910598e+00 1.17001390e+00 3.51947486e-01 -1.22846305e+00 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01d65e3c-f55d-4715-a7a7-89ad5db6e81b	question-type-driven-question-generation	1909.00140	null	https://arxiv.org/abs/1909.00140v1	https://arxiv.org/pdf/1909.00140v1.pdf	Question-type Driven Question Generation	Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type $how$ while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.	['Minghua Zhang', 'Yunfang Wu', 'Wenjie Zhou']	2019-08-31	question-type-driven-question-generation-1	https://aclanthology.org/D19-1622	https://aclanthology.org/D19-1622.pdf	ijcnlp-2019-11	['type-prediction']	['computer-code']	[ 4.40752208e-01 1.22251272e-01 1.43176332e-01 -5.66486716e-01 -1.04988766e+00 -8.96892667e-01 5.01228333e-01 1.96304917e-01 -3.44513357e-01 6.76550210e-01 3.17011625e-01 -4.16505367e-01 1.78980559e-01 -9.22387064e-01 -5.69505692e-01 9.05906409e-02 8.05054307e-01 3.62155825e-01 3.13724667e-01 -5.54043114e-01 4.31597441e-01 -3.93379241e-01 -1.64900064e+00 5.85369647e-01 1.46031499e+00 1.37222183e+00 3.24852228e-01 1.03385115e+00 -5.98609924e-01 1.06199718e+00 -1.00106502e+00 -6.99064910e-01 -9.18921009e-02 -1.08622026e+00 -1.46323442e+00 -1.56615302e-01 5.94247997e-01 -1.58461612e-02 -1.69119120e-01 8.40868831e-01 5.05304396e-01 2.38763422e-01 6.22899950e-01 -1.00862336e+00 -8.84387612e-01 3.68533224e-01 3.85498077e-01 3.15395147e-01 9.18728828e-01 2.83705264e-01 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3bd65df9-48ae-4cba-9269-db5c73b71af6	few-shot-learning-with-retrieval-augmented	2208.03299	null	https://arxiv.org/abs/2208.03299v3	https://arxiv.org/pdf/2208.03299v3.pdf	Atlas: Few-shot Learning with Retrieval Augmented Language Models	Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question answering and fact checking, massive parameter counts to store knowledge seem to be needed. Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings. In this work we present Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples. We perform evaluations on a wide range of tasks, including MMLU, KILT and NaturalQuestions, and study the impact of the content of the document index, showing that it can easily be updated. Notably, Atlas reaches over 42% accuracy on Natural Questions using only 64 examples, outperforming a 540B parameters model by 3% despite having 50x fewer parameters.	['Jane Dwivedi-Yu', 'Edouard Grave', 'Sebastian Riedel', 'Armand Joulin', 'Timo Schick', 'Fabio Petroni', 'Lucas Hosseini', 'Maria Lomeli', 'Patrick Lewis', 'Gautier Izacard']	2022-08-05	null	null	null	null	['multi-task-language-understanding', 'natural-questions']	['methodology', 'miscellaneous']	[ 1.04817124e-02 1.39277384e-01 -2.90961742e-01 5.52378111e-02 -1.14699984e+00 -6.75949812e-01 9.03376520e-01 5.30576229e-01 -9.99239683e-01 8.86489570e-01 1.73526600e-01 -5.18676579e-01 -4.48214471e-01 -6.03311241e-01 -7.98022151e-01 -2.78797179e-01 3.02313529e-02 7.16542542e-01 6.27514541e-01 -6.34827733e-01 3.80335927e-01 4.33589779e-02 -1.44962382e+00 2.93740690e-01 5.82012236e-01 6.45554483e-01 5.14860265e-02 9.91593838e-01 -4.28148121e-01 1.10483778e+00 -5.68223834e-01 -6.95082188e-01 -1.24847576e-01 5.83395697e-02 -1.03234076e+00 -3.30959171e-01 7.63098001e-01 -4.64752972e-01 -3.41941059e-01 5.57389975e-01 4.86234814e-01 5.51469207e-01 3.70956033e-01 -7.06745088e-01 -4.91366982e-01 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7d8fca9e-37b8-4c18-8dac-c6a9e28bdcf0	two-level-classification-for-dialogue-act	null	null	https://aclanthology.org/2020.coling-main.431	https://aclanthology.org/2020.coling-main.431.pdf	Two-level classification for dialogue act recognition in task-oriented dialogues	Dialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA). This approach makes it possible to benefit from the very good accuracy of generic DA classification at the first level and proposes an efficient approach for specific DA, based on high-level linguistic features. Our results show the interest of involving such features into the classifiers, outperforming all other feature sets, in particular those classically used in DA classification.	['Houda Oufaida', 'Magalie Ochs', "St{\\'e}phane Rauzy", 'Massina Abderrahmane', 'Philippe Blache']	2020-12-01	null	null	null	coling-2020-8	['dialogue-act-classification']	['natural-language-processing']	[ 4.14432921e-02 5.42440832e-01 7.33174980e-02 -5.49836755e-01 -4.13785487e-01 -6.57312810e-01 1.38607991e+00 3.27563167e-01 -6.45044446e-01 1.09784949e+00 5.36433280e-01 -2.06047162e-01 -1.89451754e-01 -7.61687219e-01 5.33283949e-01 -7.43624270e-01 1.04248591e-01 8.57235730e-01 3.89316469e-01 -8.36095572e-01 4.68971938e-01 6.20344400e-01 -1.67804694e+00 4.69118237e-01 7.10005820e-01 7.50641823e-01 -5.87865710e-02 8.05674255e-01 -6.46036565e-01 1.10993683e+00 -1.01579440e+00 -5.09218037e-01 -4.59875911e-02 -5.98689914e-01 -1.50578129e+00 1.68085292e-01 -5.84801510e-02 -1.29343331e-01 2.60740787e-01 6.83981836e-01 3.08320910e-01 1.63372949e-01 1.12645376e+00 -1.00946343e+00 2.52886802e-01 6.65858865e-01 9.26829204e-02 7.37638175e-02 1.09926617e+00 8.04859176e-02 1.04659462e+00 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a5b9d4a1-0fc9-4a8d-bb09-2fdacaad4ab9	review-of-data-analysis-in-vision-inspection	2003.09802	null	https://arxiv.org/abs/2003.09802v1	https://arxiv.org/pdf/2003.09802v1.pdf	Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology	The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial works have been conducted on the analysis of power lines inspection data. With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research. Following the typical procedure of inspection data analysis, we categorize current works in this area into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Some valuable information is also included such as data description and method performance. Further, an in-depth discussion of existing deep-learning-related analysis methods in power lines inspection is proposed. Finally, we conclude the paper with several research trends for the future of this area, such as data quality problems, small object detection, embedded application, and evaluation baseline.	['Xiren Miao', 'Hao Jiang', 'Jing Chen', 'Xinyu Liu']	2020-03-22	null	null	null	null	['small-object-detection']	['computer-vision']	[-2.67754734e-01 -3.55133921e-01 2.05150887e-01 -3.19494575e-01 -3.83486897e-01 -3.34359795e-01 -3.73876035e-01 1.41015038e-01 3.85390699e-01 3.93468052e-01 -5.29343307e-01 -3.08608532e-01 -3.98003370e-01 -8.20240736e-01 -6.68360814e-02 -1.19810832e+00 -4.35892195e-01 -9.32929888e-02 5.17575592e-02 -3.01339954e-01 2.14009225e-01 8.42249513e-01 -1.22826099e+00 2.00457558e-01 1.11211944e+00 1.42641068e+00 1.96917996e-01 4.45684671e-01 6.93524361e-01 1.09443045e+00 -1.35119736e+00 -2.33591497e-01 8.66580158e-02 -1.01206005e-01 -9.24610436e-01 9.18370724e-01 3.69117796e-01 -9.84396875e-01 -6.53863192e-01 1.35733020e+00 5.90386331e-01 2.60085836e-02 4.95762378e-01 -1.74452400e+00 -1.05917954e+00 4.03423637e-01 -8.91444027e-01 6.98929429e-01 1.86293066e-01 1.94895804e-01 1.14385462e+00 -5.98408759e-01 -2.95544505e-01 7.11908042e-01 7.72707343e-01 -2.22557500e-01 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-1.17909646e+00 -1.33758783e+00 3.86791416e-02 7.25789964e-01 -7.34173775e-01 -3.50557625e-01 9.52305179e-03]	[7.475847244262695, 1.7500332593917847]
5a5f55db-1dfb-464b-89ac-d14f902f7a8c	a-study-of-left-before-treatment-complete	2212.11879	null	https://arxiv.org/abs/2212.11879v1	https://arxiv.org/pdf/2212.11879v1.pdf	A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework	The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply Additive exPlanations (SHAP) method. The findings show that ATSA-XGB outperformed other mode configurations with an accuracy, area under the curve (AUC), sensitivity, specificity, and F1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The degree and the direction of effects of each feature were determined and explained using the SHAP method.	['Salih Tutun', 'Khalid Y. Aram', 'Abdulaziz Ahmed']	2022-12-22	null	null	null	null	['metaheuristic-optimization']	['methodology']	[-4.98033166e-02 -1.34197459e-01 -2.69415736e-01 -3.89435858e-01 -2.78904736e-01 -1.80736423e-01 1.63389482e-02 6.33857131e-01 -3.22682112e-01 9.62448359e-01 2.26180404e-01 -6.48599923e-01 -9.25432622e-01 -5.85789144e-01 -2.70043075e-01 -9.72435236e-01 -7.06031173e-02 7.07189560e-01 -5.01905918e-01 -2.19738171e-01 6.26633644e-01 6.15287781e-01 -1.64901602e+00 6.15389287e-01 1.25531912e+00 9.00893033e-01 1.99868292e-01 4.78273094e-01 -2.46603742e-01 5.96866369e-01 -4.61675584e-01 -3.03335279e-01 3.00444841e-01 -4.90729868e-01 -6.55424356e-01 -1.43337786e-01 -5.05126953e-01 5.47975749e-02 4.01455551e-01 3.26133341e-01 7.25164592e-01 1.36375755e-01 8.05809796e-01 -1.46562755e+00 -1.11162394e-01 3.15817744e-01 -6.70827210e-01 3.66894066e-01 3.12827826e-01 1.47197261e-01 4.24873620e-01 -4.47484106e-01 3.83234382e-01 1.37473214e+00 5.49937785e-01 4.48933482e-01 -9.20964897e-01 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2.80978113e-01 -1.60219297e-01 -9.24477816e-01 -5.49672246e-01 -4.42207903e-01]	[8.495078086853027, 4.87343692779541]
705a63de-89dc-43b7-a3f8-ea68c390afe6	multi-label-class-balancing-algorithm-for	2002.03238	null	https://arxiv.org/abs/2002.03238v1	https://arxiv.org/pdf/2002.03238v1.pdf	Multi-Label Class Balancing Algorithm for Action Unit Detection	Isolated facial movements, so-called Action Units, can describe combined emotions or physical states such as pain. As datasets are limited and mostly imbalanced, we present an approach incorporating a multi-label class balancing algorithm. This submission is subject to the Action Unit detection task of the Affective Behavior Analysis in-the-wild (ABAW) challenge at the IEEE Conference on Face and Gesture Recognition 2020.	['Jaspar Pahl', 'Dominik Seuss', 'Ines Rieger']	2020-02-08	null	null	null	null	['action-unit-detection']	['computer-vision']	[ 2.85640597e-01 1.59121007e-01 -7.81025648e-01 -9.60848808e-01 -5.19600093e-01 -4.79082912e-01 3.67104024e-01 -4.04529899e-01 -1.95379078e-01 7.34515131e-01 2.39007294e-01 6.01000786e-01 1.20815217e-01 -4.38756756e-02 -1.44305145e-02 -7.33499408e-01 -1.20613329e-01 8.38998333e-02 -6.91251516e-01 8.10590163e-02 -1.36847824e-01 6.80906534e-01 -1.91223466e+00 8.17052245e-01 -2.93850034e-01 1.29491365e+00 -1.37646055e+00 6.18698239e-01 -1.24262571e-02 9.76048529e-01 -5.97895384e-01 -2.69458175e-01 9.14222002e-02 -8.87664080e-01 -9.56989169e-01 2.83839524e-01 6.53426707e-01 -3.16499144e-01 1.57975912e-01 7.85781860e-01 9.79784250e-01 2.79014766e-01 7.32063949e-01 -2.23490310e+00 4.85418402e-02 1.62378594e-01 -9.91607487e-01 8.73327814e-03 8.58567894e-01 -2.37826183e-01 8.13077807e-01 -8.75275195e-01 1.18643427e+00 1.50964320e+00 6.25418782e-01 1.45751977e+00 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9b83ec71-9959-4b44-8013-2c897e9b31ee	question-answering-classification-for-amharic	null	null	https://aclanthology.org/2022.sigul-1.18	https://aclanthology.org/2022.sigul-1.18.pdf	Question Answering Classification for Amharic Social Media Community Based Questions	In this work, we build a Question Answering (QA) classification dataset from a social media platform, namely the Telegram public channel called @AskAnythingEthiopia. The channel has more than 78k subscribers and has existed since May 31, 2019. The platform allows asking questions that belong to various domains, like politics, economics, health, education, and so on. Since the questions are posed in a mixed-code, we apply different strategies to pre-process the dataset. Questions are posted in Amharic, English, or Amharic but in a Latin script. As part of the pre-processing tools, we build a Latin to Ethiopic Script transliteration tool. We collect 8k Amharic and 24K transliterated questions and develop deep learning-based questions answering classifiers that attain as high as an F-score of 57.29 in 20 different question classes or categories. The datasets and pre-processing scripts are open-sourced to facilitate further research on the Amharic community-based question answering.	['Chris Biemann', 'Abinew Ayele', 'Seid Muhie Yimam', 'Tadesse Destaw']	null	null	null	null	sigul-lrec-2022-6	['transliteration']	['natural-language-processing']	[-0.41255668 0.13016453 0.15460913 -0.53807133 -1.1137083 -0.9089743 0.46576712 0.42547458 -0.48009124 0.52881604 0.3977735 -0.897504 -0.17606667 -1.0535517 -0.49040014 0.13276875 0.44017655 0.65695727 0.39656153 -0.87754536 0.18063092 -0.01071811 -1.1194259 0.7478117 1.4482065 1.0781021 -0.23737887 1.1042645 -0.8018955 1.1982305 -0.7515933 -1.0492194 -0.09364472 -0.7457901 -1.7431649 -0.48805863 0.5740298 -0.3056336 -0.28187108 0.51082623 0.294219 -0.21358973 0.36461487 -1.1092783 -1.0955416 0.68885016 0.17806219 0.22098997 0.83117795 -0.10723427 1.342719 -0.75771326 0.44111305 1.0503637 0.5572937 0.15506858 -0.5253302 -0.6045342 -0.30488905 0.47214004 -1.0375327 -0.23423615 0.3772921 -0.44679943 0.45763272 0.54011285 0.3295937 0.84308535 0.28912285 0.48916677 1.2931982 -0.40947264 0.12165459 0.23216198 0.21679391 0.79728204 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6a468f87-c475-4732-ae7f-3856e940a757	synthetic-point-cloud-generation-for-class	2205.03738	null	https://arxiv.org/abs/2205.03738v1	https://arxiv.org/pdf/2205.03738v1.pdf	Synthetic Point Cloud Generation for Class Segmentation Applications	Maintenance of industrial facilities is a growing hazard due to the cumbersome process needed to identify infrastructure degradation. Digital Twins have the potential to improve maintenance by monitoring the continuous digital representation of infrastructure. However, the time needed to map the existing geometry makes their use prohibitive. We previously developed class segmentation algorithms to automate digital twinning, however a vast amount of annotated point clouds is needed. Currently, synthetic data generation for automated segmentation is non-existent. We used Helios++ to automatically segment point clouds from 3D models. Our research has the potential to pave the ground for efficient industrial class segmentation.	['Dr. Eva Agapaki', 'Sandeep Kamal Jalui', 'Avi Rajesh Jain', 'Maria Gonzalez Stefanelli']	2022-05-07	null	null	null	null	['point-cloud-generation']	['computer-vision']	[ 2.74108112e-01 8.94919634e-02 3.26944232e-01 -1.89273074e-01 -5.60730577e-01 -5.76448500e-01 4.73621666e-01 4.59605426e-01 2.11638838e-01 6.35868609e-01 -7.19613314e-01 -6.19690895e-01 -5.41276038e-02 -1.25382936e+00 -4.91856605e-01 -3.86655122e-01 -2.31804345e-02 1.11161625e+00 5.34887731e-01 -1.37778565e-01 5.56746364e-01 1.04211712e+00 -1.65319431e+00 -2.23650977e-01 1.02172589e+00 9.89550352e-01 6.07302010e-01 5.68282843e-01 -5.53035319e-01 1.28275249e-02 -6.91539526e-01 3.77266444e-02 3.94327134e-01 -2.44473726e-01 -7.52665877e-01 5.97769737e-01 1.18492961e-01 1.46123683e-02 1.85749203e-01 7.81742156e-01 9.64125991e-02 -1.16031066e-01 5.00979543e-01 -1.45034838e+00 4.58607078e-01 -2.44923979e-01 -3.73196870e-01 -9.90251228e-02 3.11690699e-02 1.15122698e-01 2.82405794e-01 -5.52097380e-01 7.20552623e-01 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3dcd3a75-ea8f-4210-903b-d97824cc9013	spin-structure-preserving-inner-offset	2005.13117	null	https://arxiv.org/abs/2005.13117v4	https://arxiv.org/pdf/2005.13117v4.pdf	SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition	Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Therefore, methods based on spatial transformers are extensively studied. However, chromatic difficulties in complex scenes have not been paid much attention on. In this work, we introduce a new learnable geometric-unrelated module, the Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network. This differentiable module can be inserted before any recognition architecture to ease the downstream tasks, giving neural networks the ability to actively transform input intensity rather than the existing spatial rectification. It can also serve as a complementary module to known spatial transformations and work in both independent and collaborative ways with them. Extensive experiments show that the use of SPIN results in a significant improvement on multiple text recognition benchmarks compared to the state-of-the-arts.	['ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Yi Niu', 'Fei Wu', 'Futai Zou', 'Chengwei Zhang']	2020-05-27	null	null	null	null	['color-manipulation']	['computer-vision']	[ 4.46816951e-01 -2.22798377e-01 4.19022422e-03 -4.71484005e-01 -2.11783707e-01 -6.58669949e-01 8.95830989e-01 -2.81007141e-01 -4.74334955e-01 3.24979663e-01 -3.56096216e-02 -1.74281597e-01 1.62993819e-01 -5.20943522e-01 -6.71620667e-01 -9.27891374e-01 6.83181584e-01 2.14450821e-01 2.85210758e-01 -3.02828044e-01 4.59746718e-01 8.27864826e-01 -1.15920341e+00 2.62699336e-01 9.51106131e-01 7.78539181e-01 2.16535642e-03 3.50623935e-01 -5.14517844e-01 8.10499430e-01 -4.84441847e-01 -4.78304535e-01 4.50265706e-01 -3.04210067e-01 -4.79666412e-01 2.12688744e-01 8.21758151e-01 -2.71367639e-01 -5.26359260e-01 9.64801908e-01 5.19564509e-01 1.82629749e-01 5.90204597e-01 -1.08971512e+00 -9.60930645e-01 5.71961761e-01 -8.56061816e-01 -8.43603257e-03 1.59817442e-01 3.16554010e-02 7.94299662e-01 -1.06540692e+00 3.87573093e-01 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8300dd9d-d4bc-4059-a603-f83aa0eec2be	learning-to-infer-3d-object-models-from	2006.06130	null	https://arxiv.org/abs/2006.06130v3	https://arxiv.org/pdf/2006.06130v3.pdf	ROOTS: Object-Centric Representation and Rendering of 3D Scenes	A crucial ability of human intelligence is to build up models of individual 3D objects from partial scene observations. Recent works achieve object-centric generation but without the ability to infer the representation, or achieve 3D scene representation learning but without object-centric compositionality. Therefore, learning to represent and render 3D scenes with object-centric compositionality remains elusive. In this paper, we propose a probabilistic generative model for learning to build modular and compositional 3D object models from partial observations of a multi-object scene. The proposed model can (i) infer the 3D object representations by learning to search and group object areas and also (ii) render from an arbitrary viewpoint not only individual objects but also the full scene by compositing the objects. The entire learning process is unsupervised and end-to-end. In experiments, in addition to generation quality, we also demonstrate that the learned representation permits object-wise manipulation and novel scene generation, and generalizes to various settings. Results can be found on our project website: https://sites.google.com/view/roots3d	['Sungjin Ahn', 'Fei Deng', 'Chang Chen']	2020-06-11	null	null	null	null	['scene-generation']	['computer-vision']	[ 2.82165051e-01 2.50294328e-01 2.15904698e-01 -5.17725766e-01 -5.62444150e-01 -6.42733574e-01 9.01810467e-01 -9.23150778e-02 2.97273159e-01 3.12693983e-01 7.06046373e-02 9.25172865e-02 -1.19691327e-01 -8.22621226e-01 -1.03466594e+00 -4.94811952e-01 9.74394977e-02 1.02803004e+00 2.23022193e-01 1.90593496e-01 2.41049245e-01 8.75167906e-01 -1.77841830e+00 4.36377943e-01 7.54361570e-01 8.73335361e-01 7.63675630e-01 8.03908050e-01 -2.42187425e-01 7.44845808e-01 -4.91482377e-01 -6.64360076e-02 5.26700914e-01 -2.73830444e-01 -6.27854884e-01 9.08811331e-01 8.54968429e-01 -4.64998901e-01 -1.39649212e-01 8.06529224e-01 3.44322711e-01 1.01399466e-01 8.29600513e-01 -1.27812123e+00 -1.10519493e+00 3.82549465e-01 -3.21372122e-01 -4.49582398e-01 5.30424237e-01 3.68179083e-01 8.86961102e-01 -9.93159831e-01 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059e808b-f1c0-41c0-ac77-42887bce87b8	video-event-restoration-based-on-keyframes	2304.05112	null	https://arxiv.org/abs/2304.05112v1	https://arxiv.org/pdf/2304.05112v1.pdf	Video Event Restoration Based on Keyframes for Video Anomaly Detection	Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or frame prediction. However, the lack of mining and learning of higher-level visual features and temporal context relationships in videos limits the further performance of these two approaches. Inspired by video codec theory, we introduce a brand-new VAD paradigm to break through these limitations: First, we propose a new task of video event restoration based on keyframes. Encouraging DNN to infer missing multiple frames based on video keyframes so as to restore a video event, which can more effectively motivate DNN to mine and learn potential higher-level visual features and comprehensive temporal context relationships in the video. To this end, we propose a novel U-shaped Swin Transformer Network with Dual Skip Connections (USTN-DSC) for video event restoration, where a cross-attention and a temporal upsampling residual skip connection are introduced to further assist in restoring complex static and dynamic motion object features in the video. In addition, we propose a simple and effective adjacent frame difference loss to constrain the motion consistency of the video sequence. Extensive experiments on benchmarks demonstrate that USTN-DSC outperforms most existing methods, validating the effectiveness of our method.	['Xiaotao Liu', 'Peng Wu', 'Zhaoyang Wu', 'Jing Liu', 'Zhiwei Yang']	2023-04-11	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.pdf	cvpr-2023-1	['video-anomaly-detection']	['computer-vision']	[ 1.83677763e-01 -4.43148822e-01 -2.85073698e-01 -1.96233451e-01 -4.02687453e-02 -8.02939609e-02 5.25718212e-01 -1.74410984e-01 -2.60059029e-01 5.32155633e-01 4.33576733e-01 -3.68929118e-01 2.03423332e-02 -4.51311797e-01 -1.02817035e+00 -5.78400612e-01 -1.81568578e-01 -5.00646830e-01 5.24653912e-01 -7.75907785e-02 2.15447605e-01 4.24243987e-01 -1.64214492e+00 4.51522350e-01 7.39366591e-01 1.31259286e+00 4.85499978e-01 4.68478590e-01 -1.86287954e-01 1.57217252e+00 -3.64279658e-01 -9.15877521e-02 3.12862396e-01 -5.19149363e-01 -6.12424254e-01 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5378be86-eea7-452b-9f42-6a480e083a78	learning-to-learn-generative-programs-with	2007.03132	null	https://arxiv.org/abs/2007.03132v2	https://arxiv.org/pdf/2007.03132v2.pdf	Learning to learn generative programs with Memoised Wake-Sleep	We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs. As generative models, these programs capture compositional structures in a naturally explainable form. To tackle the challenge of performing program induction as an 'inner-loop' to learning, we propose the Memoised Wake-Sleep (MWS) algorithm, which extends Wake Sleep by explicitly storing and reusing the best programs discovered by the inference network throughout training. We use MWS to learn accurate, explainable models in three challenging domains: stroke-based character modelling, cellular automata, and few-shot learning in a novel dataset of real-world string concepts.	['Tuan Anh Le', 'Luke B. Hewitt', 'Joshua B. Tenenbaum']	2020-07-06	null	null	null	null	['program-induction', 'explainable-models']	['computer-code', 'computer-vision']	[ 6.25387311e-01 3.21745634e-01 -3.77013564e-01 -4.74050999e-01 -3.23828578e-01 -4.34757680e-01 7.65585363e-01 -7.74747180e-03 2.11509522e-02 6.67310059e-01 1.79941133e-01 -5.86291671e-01 5.80680557e-02 -1.18704283e+00 -1.33181775e+00 -4.22964334e-01 -4.88058776e-02 9.80950952e-01 3.14432949e-01 -1.26487300e-01 3.29931945e-01 1.34285897e-01 -1.71959686e+00 6.05533838e-01 8.44688296e-01 7.33327642e-02 2.31073022e-01 1.13267982e+00 -3.76867026e-01 1.53165174e+00 -4.20648217e-01 -4.70314085e-01 -4.02511597e-01 -8.71696770e-01 -7.14303076e-01 -5.66917121e-01 -1.86132360e-02 -9.21370909e-02 -5.12209594e-01 1.01279962e+00 5.70450760e-02 4.12765980e-01 6.11317575e-01 -1.39270234e+00 -9.34718549e-01 1.16418207e+00 1.80053577e-01 3.63214873e-02 4.12208021e-01 3.99887890e-01 9.98892188e-01 -5.81342459e-01 9.07949507e-01 1.30044568e+00 7.93365479e-01 1.03974736e+00 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-1.59118012e-01 2.32613787e-01 2.62221664e-01 -3.44518334e-01 -1.99159667e-01 5.62979221e-01]	[8.472187042236328, 7.29198694229126]
bf6b7fa3-ba61-456d-a68f-0ba8ea3eff78	towards-social-engaging-peer-learning	2007.11346	null	https://arxiv.org/abs/2007.11346v1	https://arxiv.org/pdf/2007.11346v1.pdf	Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children	Social robots and interactive computer applications have the potential to foster early language development in young children by acting as peer learning companions. However, studies have found that children only trust robots which behave in a natural and interpersonal manner. To help robots come across as engaging and attentive peer learning companions, we develop models to predict whether the listener will lose attention (Listener Disengagement Prediction, LDP) and the extent to which a robot should generate backchanneling responses (Backchanneling Extent Prediction, BEP) in the next few seconds. We pose LDP and BEP as time series classification problems and conduct several experiments to assess the impact of different time series characteristics and feature sets on the predictive performance of our model. Using statistics & machine learning, we also examine which socio-demographic factors influence the amount of time children spend backchanneling and listening to their peers. To lend interpretability to our models, we also analyzed critical features responsible for their predictive performance. Our experiments revealed the utility of multimodal features such as pupil dilation, blink rate, head movements, facial action units which have never been used before. We also found that the dynamics of time series features are rich predictors of listener disengagement and backchanneling.	['Mononito Goswami', 'Maitree Leekha', 'Minkush Manuja']	2020-07-22	null	null	null	null	['pupil-dilation']	['computer-vision']	[-1.66391134e-01 7.68565357e-01 1.20089747e-01 -5.09572446e-01 1.29096434e-01 -3.31578761e-01 4.95642394e-01 4.72572535e-01 -4.22264397e-01 4.39343721e-01 3.09264839e-01 6.22328185e-02 -1.55054450e-01 -5.17923653e-01 -6.72317982e-01 -5.24493217e-01 -4.63485897e-01 -3.78783257e-03 -5.88177331e-02 -2.03904465e-01 1.58944577e-01 3.43895614e-01 -2.01230168e+00 -2.64955554e-02 8.47822070e-01 3.89042974e-01 1.91717118e-01 8.63663554e-01 3.63935918e-01 1.41008961e+00 -6.75660014e-01 -2.30655342e-01 -2.95552641e-01 -1.24057829e-01 -6.99539661e-01 -3.42705488e-01 -5.44638969e-02 -6.32679343e-01 -2.18526512e-01 5.97823381e-01 5.61064065e-01 1.90865949e-01 8.84896159e-01 -1.83174920e+00 -5.81725240e-01 1.09006023e+00 -4.72621202e-01 2.02795088e-01 1.05716896e+00 -4.88710329e-02 7.28889465e-01 -3.89019668e-01 4.37571913e-01 1.49129009e+00 6.41398847e-01 6.56030834e-01 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-5.76974511e-01 -2.28864536e-01 5.67375302e-01 -6.65710211e-01 -4.49547648e-01 -2.30173558e-01]	[10.456947326660156, 8.541485786437988]
5e360a07-0481-4c89-82c0-8cf0f5ec1962	efficient-direct-density-ratio-estimation-for	null	null	http://papers.nips.cc/paper/3387-efficient-direct-density-ratio-estimation-for-non-stationarity-adaptation-and-outlier-detection	http://papers.nips.cc/paper/3387-efficient-direct-density-ratio-estimation-for-non-stationarity-adaptation-and-outlier-detection.pdf	Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection	We address the problem of estimating the ratio of two probability density functions (a.k.a.~the importance). The importance values can be used for various succeeding tasks such as non-stationarity adaptation or outlier detection. In this paper, we propose a new importance estimation method that has a closed-form solution; the leave-one-out cross-validation score can also be computed analytically. Therefore, the proposed method is computationally very efficient and numerically stable. We also elucidate theoretical properties of the proposed method such as the convergence rate and approximation error bound. Numerical experiments show that the proposed method is comparable to the best existing method in accuracy, while it is computationally more efficient than competing approaches.	['Masashi Sugiyama', 'Takafumi Kanamori', 'Shohei Hido']	2008-12-01	null	null	null	neurips-2008-12	['density-ratio-estimation']	['methodology']	[-3.01569998e-01 -4.09772635e-01 -1.64428741e-01 -3.25720757e-01 -1.24977434e+00 -3.86157602e-01 1.00853130e-01 3.03741872e-01 -5.25068939e-01 1.26953578e+00 -3.72347176e-01 -2.63531148e-01 -3.65591347e-01 -3.20847064e-01 -5.76548576e-01 -9.17729437e-01 -2.19970480e-01 4.26507890e-01 3.01549494e-01 1.76065966e-01 5.17145991e-01 6.83424771e-01 -1.71956229e+00 -6.85478270e-01 1.14629292e+00 1.30715787e+00 -3.03154618e-01 6.83912933e-01 1.70785353e-01 5.17750978e-01 -7.82873511e-01 -2.88045138e-01 1.00582123e-01 -2.82140553e-01 -5.53671539e-01 -1.01945147e-01 1.78856745e-01 -1.36965930e-01 4.14070874e-01 1.24700487e+00 4.49842334e-01 4.66094553e-01 1.06454122e+00 -1.59501386e+00 -2.76984543e-01 6.76316991e-02 -9.32210624e-01 6.74003839e-01 2.56100833e-01 -3.02959740e-01 8.47929001e-01 -1.01933646e+00 9.68649797e-03 1.02120864e+00 9.74560618e-01 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c7e0ede6-0fe9-47f9-8da6-dcad7ea082a3	low-light-video-enhancement-by-learning-on	2210.04290	null	https://arxiv.org/abs/2210.04290v1	https://arxiv.org/pdf/2210.04290v1.pdf	Low Light Video Enhancement by Learning on Static Videos with Cross-Frame Attention	The design of deep learning methods for low light video enhancement remains a challenging problem owing to the difficulty in capturing low light and ground truth video pairs. This is particularly hard in the context of dynamic scenes or moving cameras where a long exposure ground truth cannot be captured. We approach this problem by training a model on static videos such that the model can generalize to dynamic videos. Existing methods adopting this approach operate frame by frame and do not exploit the relationships among neighbouring frames. We overcome this limitation through a selfcross dilated attention module that can effectively learn to use information from neighbouring frames even when dynamics between the frames are different during training and test times. We validate our approach through experiments on multiple datasets and show that our method outperforms other state-of-the-art video enhancement algorithms when trained only on static videos.	['Rajiv Soundararajan', 'Sameer Malik', 'Shivam Chhirolya']	2022-10-09	null	null	null	null	['video-enhancement']	['computer-vision']	[ 3.91114444e-01 -4.12646621e-01 1.62728131e-01 -2.20384926e-01 -5.34411907e-01 -4.52305466e-01 4.12321866e-01 -3.83588552e-01 -6.93015218e-01 8.10798347e-01 1.41642436e-01 1.02710925e-01 -7.32122585e-02 -6.39432788e-01 -1.06524813e+00 -7.76667535e-01 -1.06580667e-01 -2.40370214e-01 4.77385491e-01 -1.97769299e-01 3.65414508e-02 2.45781228e-01 -1.60277212e+00 4.61721241e-01 4.06734049e-01 6.95576191e-01 4.81706113e-01 9.41999257e-01 3.10094386e-01 9.92341876e-01 -4.04501975e-01 -3.88855696e-01 6.68752134e-01 -4.52274472e-01 -6.04789734e-01 4.42375243e-01 8.68760645e-01 -8.74496758e-01 -7.38722742e-01 9.81981456e-01 6.04490221e-01 4.09159541e-01 8.07898343e-02 -1.16295087e+00 -4.54764098e-01 8.45442712e-02 -6.55971885e-01 6.56194448e-01 3.30810040e-01 2.19073892e-01 5.33839762e-01 -7.25782216e-01 7.09709585e-01 9.03748214e-01 8.78956378e-01 6.95462704e-01 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c3e01462-903f-4522-b886-a0bdac2ff5d7	a-multi-head-relevance-weighting-framework	2107.14793	null	https://arxiv.org/abs/2107.14793v1	https://arxiv.org/pdf/2107.14793v1.pdf	A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representations	In this work, we propose a multi-head relevance weighting framework to learn audio representations from raw waveforms. The audio waveform, split into windows of short duration, are processed with a 1-D convolutional layer of cosine modulated Gaussian filters acting as a learnable filterbank. The key novelty of the proposed framework is the introduction of multi-head relevance on the learnt filterbank representations. Each head of the relevance network is modelled as a separate sub-network. These heads perform representation enhancement by generating weight masks for different parts of the time-frequency representation learnt by the parametric acoustic filterbank layer. The relevance weighted representations are fed to a neural classifier and the whole system is trained jointly for the audio classification objective. Experiments are performed on the DCASE2020 Task 1A challenge as well as the Urban Sound Classification (USC) tasks. In these experiments, the proposed approach yields relative improvements of 10% and 23% respectively for the DCASE2020 and USC datasets over the mel-spectrogram baseline. Also, the analysis of multi-head relevance weights provides insights on the learned representations.	['Sriram Ganapathy', 'Purvi Agrawal', 'Debottam Dutta']	2021-07-30	null	null	null	null	['sound-classification']	['audio']	[ 5.08327067e-01 1.72218665e-01 3.08751941e-01 -3.52398932e-01 -1.39449298e+00 -2.99774319e-01 4.36992437e-01 2.17464358e-01 -3.55395526e-01 3.81815970e-01 5.57924628e-01 8.63306373e-02 -2.18282327e-01 -3.76214653e-01 -4.99969691e-01 -6.13465250e-01 -6.16946518e-01 -2.85698563e-01 2.03546733e-01 -2.01783657e-01 1.15690148e-02 4.21269506e-01 -1.91173148e+00 5.79180717e-01 4.13483858e-01 1.56069791e+00 2.88369864e-01 1.26324022e+00 5.35363555e-01 5.53130090e-01 -9.38906550e-01 3.35725993e-02 -3.09678372e-02 -4.02410105e-02 -6.61212504e-01 -2.23134726e-01 6.01370037e-01 1.04679577e-01 -6.11614361e-02 8.09150279e-01 8.36363196e-01 6.51977599e-01 6.11648619e-01 -9.64511693e-01 -3.46787840e-01 1.03397512e+00 7.72906514e-03 5.55704296e-01 3.33307236e-01 -1.70365468e-01 1.47281075e+00 -1.20121264e+00 -3.43971304e-03 1.31099880e+00 7.09975004e-01 3.27896386e-01 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b06993d8-71ec-46f7-8407-a21212de9922	predicting-foreign-language-usage-from	null	null	https://aclanthology.org/N18-2096	https://aclanthology.org/N18-2096.pdf	Predicting Foreign Language Usage from English-Only Social Media Posts	Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English. We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets. Unlike native language identification task, we rely on large amounts of informal social media communications rather than ESL essays. We contrast the predictive power of the state-of-the-art machine learning models trained on lexical, syntactic, and stylistic signals with neural network models learned from word, character and byte representations extracted from English only tweets. We report that content, style and syntax are the most predictive of non-English languages that users speak on Twitter. Neural network models learned from byte representations of user content combined with transfer learning yield the best performance. Finally, by analyzing cross-lingual transfer {--} the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 12 languages in social media.	['Lawrence Phillips', 'Svitlana Volkova', 'Stephen Ranshous']	2018-06-01	null	null	null	naacl-2018-6	['native-language-identification']	['natural-language-processing']	[-4.98361528e-01 -2.27372900e-01 -6.98650360e-01 -4.04163480e-01 -1.02481401e+00 -7.29907632e-01 8.64024818e-01 2.55690724e-01 -7.96167195e-01 6.51035190e-01 7.16094196e-01 -7.12501466e-01 4.36855316e-01 -5.58411360e-01 -5.95055997e-01 -1.32521885e-02 1.69685587e-01 4.51409161e-01 -3.46666634e-01 -6.25799179e-01 2.25582063e-01 5.76881468e-02 -1.07856381e+00 6.05132759e-01 1.03744245e+00 5.09173274e-01 2.49651372e-01 5.09284973e-01 -7.25983500e-01 1.03130829e+00 -3.93887222e-01 -5.48606098e-01 -1.99499667e-01 -2.95213193e-01 -7.94456124e-01 -4.85530980e-02 5.27043104e-01 -2.22467296e-02 -1.77444130e-01 9.87362683e-01 3.29288989e-01 -3.67061943e-01 9.78709340e-01 -6.68082237e-01 -1.06857181e+00 1.21843505e+00 -6.40838265e-01 4.97255981e-01 5.83455265e-01 2.42679287e-03 1.04477346e+00 -9.76227582e-01 7.79357910e-01 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0b486f05-a500-4fed-b362-d3ff158ca3be	a-complete-recipe-for-stochastic-gradient	1506.04696	null	http://arxiv.org/abs/1506.04696v2	http://arxiv.org/pdf/1506.04696v2.pdf	A Complete Recipe for Stochastic Gradient MCMC	Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic simulations. However, such stochastic gradient MCMC samplers have lagged behind their full-data counterparts in terms of the complexity of dynamics considered since proving convergence in the presence of the stochastic gradient noise is non-trivial. Even with simple dynamics, significant physical intuition is often required to modify the dynamical system to account for the stochastic gradient noise. In this paper, we provide a general recipe for constructing MCMC samplers--including stochastic gradient versions--based on continuous Markov processes specified via two matrices. We constructively prove that the framework is complete. That is, any continuous Markov process that provides samples from the target distribution can be written in our framework. We show how previous continuous-dynamic samplers can be trivially "reinvented" in our framework, avoiding the complicated sampler-specific proofs. We likewise use our recipe to straightforwardly propose a new state-adaptive sampler: stochastic gradient Riemann Hamiltonian Monte Carlo (SGRHMC). Our experiments on simulated data and a streaming Wikipedia analysis demonstrate that the proposed SGRHMC sampler inherits the benefits of Riemann HMC, with the scalability of stochastic gradient methods.	['Tianqi Chen', 'Yi-An Ma', 'Emily B. Fox']	2015-06-15	a-complete-recipe-for-stochastic-gradient-1	http://papers.nips.cc/paper/5891-a-complete-recipe-for-stochastic-gradient-mcmc	http://papers.nips.cc/paper/5891-a-complete-recipe-for-stochastic-gradient-mcmc.pdf	neurips-2015-12	['physical-intuition']	['reasoning']	[-5.75921834e-02 -2.00190559e-01 1.16861589e-01 4.77465391e-02 -8.96025836e-01 -5.92575848e-01 9.23757255e-01 -2.42046878e-01 -4.37334388e-01 9.48789358e-01 1.25581160e-01 -7.62996197e-01 -8.68074223e-02 -9.26418364e-01 -7.66283691e-01 -1.08643115e+00 -3.46982688e-01 7.56834209e-01 2.98088491e-01 -1.04939058e-01 2.21234053e-01 3.32132697e-01 -1.07589972e+00 -3.69017631e-01 7.83838093e-01 6.00181036e-02 7.37450272e-02 1.20244634e+00 -8.57165456e-03 4.93182600e-01 2.17863619e-02 -3.08975011e-01 1.10861458e-01 -8.20285141e-01 -6.32971227e-01 -2.05643162e-01 -2.08235886e-02 -3.25379342e-01 -2.88305342e-01 1.12911677e+00 4.84007388e-01 1.92259386e-01 8.10155034e-01 -9.84449625e-01 7.68001005e-02 6.56352460e-01 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458d3dd1-a8fd-449c-91f7-24659887f01c	pose2pose-3d-positional-pose-guided-3d	2011.11534	null	https://arxiv.org/abs/2011.11534v4	https://arxiv.org/pdf/2011.11534v4.pdf	Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation	Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been carefully considered when predicting the 3D wrists. Second, previous works utilize body features for the 3D fingers, where the body feature barely contains finger information. To resolve the limitations, we present Hand4Whole, which has two strong points over previous works. First, we design Pose2Pose, a module that utilizes joint features for 3D joint rotations. Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain. Second, Hand4Whole discards the body feature when predicting 3D finger rotations. Our Hand4Whole is trained in an end-to-end manner and produces much better 3D hand results than previous whole-body 3D human mesh estimation methods. The codes are available here at https://github.com/mks0601/Hand4Whole_RELEASE.	['Kyoung Mu Lee', 'Hongsuk Choi', 'Gyeongsik Moon']	2020-11-23	null	null	null	null	['3d-human-reconstruction']	['computer-vision']	[-6.37589335e-01 1.85115233e-01 -4.30791646e-01 7.85250738e-02 -3.81543845e-01 -3.20091039e-01 2.01514348e-01 -5.30506968e-01 -1.02895148e-01 4.46816623e-01 3.77165377e-01 1.44518152e-01 4.43712287e-02 -6.54792190e-01 -5.24178624e-01 -7.73472711e-02 8.28820746e-03 9.18066680e-01 4.03104663e-01 -2.69035429e-01 -7.82095790e-02 7.12169588e-01 -1.35139513e+00 -1.06708305e-02 2.85605580e-01 8.72973025e-01 -2.44555131e-01 6.39678597e-01 2.36931711e-01 1.32809803e-01 -1.93360671e-01 -6.17007852e-01 4.84333128e-01 -3.49020720e-01 -6.49476647e-01 -9.66212302e-02 3.43639940e-01 -6.97410285e-01 -2.29409397e-01 2.87848264e-01 8.50637376e-01 -4.14203480e-02 7.23574519e-01 -1.18388820e+00 7.89936110e-02 1.58403799e-01 -8.42705667e-01 -5.71208000e-01 9.19265687e-01 -1.48066180e-02 8.19477260e-01 -1.38087618e+00 1.01344895e+00 1.43071187e+00 1.06248713e+00 6.61474466e-01 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cbc0bf03-60bb-4a96-81d8-72ba30d61597	crowd-powered-face-manipulation-detection	2201.13084	null	https://arxiv.org/abs/2201.13084v1	https://arxiv.org/pdf/2201.13084v1.pdf	Crowd-powered Face Manipulation Detection: Fusing Human Examiner Decisions	We investigate the potential of fusing human examiner decisions for the task of digital face manipulation detection. To this end, various decision fusion methods are proposed incorporating the examiners' decision confidence, experience level, and their time to take a decision. Conducted experiments are based on a psychophysical evaluation of digital face image manipulation detection capabilities of humans in which different manipulation techniques were applied, i.e. face morphing, face swapping and retouching. The decisions of 223 participants were fused to simulate crowds of up to seven human examiners. Experimental results reveal that (1) despite the moderate detection performance achieved by single human examiners, a high accuracy can be obtained through decision fusion and (2) a weighted fusion which takes the examiners' decision confidence into account yields the most competitive detection performance.	['Christoph Busch', 'Pawel Drozdowski', 'Mathias Ibsen', 'Robert Nichols', 'Christian Rathgeb']	2022-01-31	null	null	null	null	['image-manipulation-detection']	['computer-vision']	[ 2.29268357e-01 5.84334172e-02 4.40081358e-01 -3.40727478e-01 -5.00300944e-01 -5.87031722e-01 4.09431607e-01 2.70113889e-02 -5.77481270e-01 3.19787383e-01 -1.79568335e-01 -2.02697292e-01 -9.40414742e-02 -2.16076970e-01 -1.66989431e-01 -5.11073291e-01 3.61937642e-01 -8.44942704e-02 2.87595421e-01 -9.82019603e-02 5.99994898e-01 7.72361577e-01 -2.04671597e+00 3.09042990e-01 1.03211343e+00 1.09670293e+00 -1.50494099e-01 7.59093761e-01 3.05160582e-01 6.69309556e-01 -8.84489834e-01 -8.53747785e-01 7.18389869e-01 -7.64345080e-02 -2.13411957e-01 4.71441269e-01 8.24473917e-01 -6.47550046e-01 -2.01040283e-01 1.14440155e+00 8.05428743e-01 2.83273816e-01 6.19466126e-01 -1.48787236e+00 -8.34739149e-01 1.29652739e-01 -7.59876728e-01 2.67546445e-01 1.13132465e+00 6.11566722e-01 1.66036829e-01 -9.04364645e-01 1.18336499e-01 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-7.13592350e-01 -1.10119545e+00 -7.45429456e-01 1.84916332e-01 1.22389533e-01 -1.02729940e+00 -7.03652799e-01 8.30888152e-02]	[13.088458061218262, 1.0404903888702393]
ce470f40-320b-4554-a285-55e6cdf31326	domain-adaptation-for-structured-output-via	1901.05427	null	https://arxiv.org/abs/1901.05427v4	https://arxiv.org/pdf/1901.05427v4.pdf	Domain Adaptation for Structured Output via Discriminative Patch Representations	Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks. However, models trained on one data domain may not generalize well to other domains without annotations for model finetuning. To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain. We propose to learn discriminative feature representations of patches in the source domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space. With such representations as guidance, we use an adversarial learning scheme to push the feature representations of target patches in the clustered space closer to the distributions of source patches. In addition, we show that our framework is complementary to existing domain adaptation techniques and achieves consistent improvements on semantic segmentation. Extensive ablations and results are demonstrated on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.	['Yi-Hsuan Tsai', 'Kihyuk Sohn', 'Samuel Schulter', 'Manmohan Chandraker']	2019-01-16	domain-adaptation-for-structured-output-via-2	http://openaccess.thecvf.com/content_ICCV_2019/html/Tsai_Domain_Adaptation_for_Structured_Output_via_Discriminative_Patch_Representations_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Tsai_Domain_Adaptation_for_Structured_Output_via_Discriminative_Patch_Representations_ICCV_2019_paper.pdf	iccv-2019-10	['synthetic-to-real-translation']	['computer-vision']	[ 6.42253816e-01 3.72581571e-01 -1.91149399e-01 -7.18977094e-01 -9.60856736e-01 -9.69241261e-01 4.39800411e-01 -2.05889285e-01 -1.11564152e-01 7.50271022e-01 -8.50382969e-02 -6.44533783e-02 2.03856379e-01 -8.28280985e-01 -1.11083746e+00 -7.40341663e-01 2.27932662e-01 6.54058456e-01 4.32545245e-01 1.28699029e-02 -3.06739360e-02 3.85178328e-01 -1.07931089e+00 4.10207242e-01 9.45717931e-01 7.66713679e-01 1.60409883e-01 5.00108004e-01 -1.74304798e-01 3.08292657e-01 -6.71397507e-01 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e389de76-b06b-421b-ba8c-5ae73ddcabe2	safe-reinforcement-learning-via-shielding-for	2204.00755	null	https://arxiv.org/abs/2204.00755v2	https://arxiv.org/pdf/2204.00755v2.pdf	Safe Reinforcement Learning via Shielding under Partial Observability	Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the safety of an action. A so-called shield forces the RL agent to select only safe actions. However, for adoption in various applications, one must look beyond enforcing safety and also ensure the applicability of RL with good performance. We extend the applicability of shields via tight integration with state-of-the-art deep RL, and provide an extensive, empirical study in challenging, sparse-reward environments under partial observability. We show that a carefully integrated shield ensures safety and can improve the convergence rate and final performance of RL agents. We furthermore show that a shield can be used to bootstrap state-of-the-art RL agents: they remain safe after initial learning in a shielded setting, allowing us to disable a potentially too conservative shield eventually.	['Ufuk Topcu', 'Sebastian Junges', 'Nils Jansen', 'Steven Carr']	2022-04-02	null	null	null	null	['safe-exploration']	['robots']	[ 4.64006215e-02 4.38606709e-01 -2.73191959e-01 -9.16478038e-02 -8.62939835e-01 -8.64968777e-01 5.88694453e-01 -1.22984173e-03 -8.83344412e-01 1.15775752e+00 -9.46326554e-03 -4.51169103e-01 -1.22373633e-01 -7.84739971e-01 -1.04357588e+00 -8.88558865e-01 -5.67462385e-01 4.92896855e-01 3.30074996e-01 -4.89080489e-01 4.98510376e-02 4.70128268e-01 -1.42527759e+00 -2.45403513e-01 7.90924430e-01 6.87074006e-01 -5.53309880e-02 6.30855739e-01 7.76914775e-01 1.14771914e+00 -6.07551455e-01 2.24850670e-01 6.31426632e-01 -4.12766963e-01 -8.79940629e-01 -3.22335176e-02 -2.39907593e-01 -7.60952890e-01 -1.59388736e-01 1.10655773e+00 4.18210566e-01 4.07753468e-01 1.70688868e-01 -1.41601968e+00 -3.62702012e-02 8.80417645e-01 -3.80884379e-01 -1.50684640e-01 2.44127542e-01 7.36516654e-01 8.99413407e-01 1.35035589e-01 6.43966734e-01 1.16846240e+00 3.67162853e-01 8.67882907e-01 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219d53b6-684c-4b70-a384-14e0d04eb031	defending-black-box-classifiers-by-bayesian	2306.16979	null	https://arxiv.org/abs/2306.16979v1	https://arxiv.org/pdf/2306.16979v1.pdf	Defending Black-box Classifiers by Bayesian Boundary Correction	Classifiers based on deep neural networks have been recently challenged by Adversarial Attack, where the widely existing vulnerability has invoked the research in defending them from potential threats. Given a vulnerable classifier, existing defense methods are mostly white-box and often require re-training the victim under modified loss functions/training regimes. While the model/data/training specifics of the victim are usually unavailable to the user, re-training is unappealing, if not impossible for reasons such as limited computational resources. To this end, we propose a new black-box defense framework. It can turn any pre-trained classifier into a resilient one with little knowledge of the model specifics. This is achieved by new joint Bayesian treatments on the clean data, the adversarial examples and the classifier, for maximizing their joint probability. It is further equipped with a new post-train strategy which keeps the victim intact. We name our framework Bayesian Boundary Correction (BBC). BBC is a general and flexible framework that can easily adapt to different data types. We instantiate BBC for image classification and skeleton-based human activity recognition, for both static and dynamic data. Exhaustive evaluation shows that BBC has superior robustness and can enhance robustness without severely hurting the clean accuracy, compared with existing defense methods.	['Yunfeng Diao', 'He Wang']	2023-06-29	null	null	null	null	['adversarial-attack', 'activity-recognition', 'human-activity-recognition', 'human-activity-recognition']	['adversarial', 'computer-vision', 'computer-vision', 'time-series']	[ 4.41747606e-01 6.72118785e-03 -8.95674434e-03 -1.10202909e-01 -5.78446090e-01 -7.27601886e-01 6.71785951e-01 -1.51025787e-01 -5.87634563e-01 7.22448170e-01 -7.90223777e-02 -2.61810839e-01 -2.22943395e-01 -8.24105740e-01 -6.62614644e-01 -1.04467773e+00 -1.76575825e-01 2.53722906e-01 4.90597546e-01 -2.69385964e-01 8.84924084e-02 7.02395439e-01 -1.31391764e+00 1.71894357e-01 6.41523719e-01 9.81921315e-01 -4.27930564e-01 6.56205475e-01 3.82671535e-01 5.27906418e-01 -7.76417315e-01 -7.06570089e-01 4.67422605e-01 -9.76672173e-02 -6.15708888e-01 -3.49724919e-01 3.50219369e-01 -3.15576077e-01 -5.55918694e-01 1.31005228e+00 4.89304960e-01 3.65548134e-02 6.77298784e-01 -1.37598217e+00 -2.66970217e-01 6.21307850e-01 -5.37514746e-01 1.90335676e-01 4.59511504e-02 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dbbeaeac-85cf-44c0-8f65-66ddce4fbb2d	efficient-multi-scale-attention-module-with	2305.13563	null	https://arxiv.org/abs/2305.13563v2	https://arxiv.org/pdf/2305.13563v2.pdf	Efficient Multi-Scale Attention Module with Cross-Spatial Learning	Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. However, modeling the cross-channel relationships with channel dimensionality reduction may bring side effect in extracting deep visual representations. In this paper, a novel efficient multi-scale attention (EMA) module is proposed. Focusing on retaining the information on per channel and decreasing the computational overhead, we reshape the partly channels into the batch dimensions and group the channel dimensions into multiple sub-features which make the spatial semantic features well-distributed inside each feature group. Specifically, apart from encoding the global information to re-calibrate the channel-wise weight in each parallel branch, the output features of the two parallel branches are further aggregated by a cross-dimension interaction for capturing pixel-level pairwise relationship. We conduct extensive ablation studies and experiments on image classification and object detection tasks with popular benchmarks (e.g., CIFAR-100, ImageNet-1k, MS COCO and VisDrone2019) for evaluating its performance.	['Jian Zhan', 'Guozhong Zhang', 'Mingzhu Luo', 'Zhijie Huang', 'Huaiyong Guo', 'Su He', 'Daliang Ouyang']	2023-05-23	null	null	null	null	['dimensionality-reduction']	['methodology']	[ 1.75819024e-02 -3.40776294e-01 1.72067091e-01 -5.20469785e-01 -6.57761395e-01 -3.59939069e-01 3.92230690e-01 -1.21072315e-01 -5.52755713e-01 5.21738887e-01 2.74833918e-01 -1.20972291e-01 -1.34416565e-01 -5.96087158e-01 -8.59277725e-01 -8.85884345e-01 -2.42089644e-01 -3.93061668e-01 1.14273071e-01 7.30576888e-02 2.89899617e-01 5.01814306e-01 -1.54165661e+00 6.63067341e-01 6.66703045e-01 1.40951633e+00 3.62977505e-01 5.10954797e-01 -4.00827169e-01 4.42084044e-01 -5.50865591e-01 -2.48296723e-01 3.65033150e-01 -3.51147562e-01 -6.32927954e-01 2.37679318e-01 4.90973026e-01 -4.08140361e-01 -5.83924055e-01 1.24403083e+00 5.93163073e-01 -1.19038545e-01 6.02755606e-01 -1.26835835e+00 -8.58846247e-01 6.64947033e-01 -1.01260912e+00 5.58213532e-01 -3.16594809e-01 3.51249814e-01 9.74595487e-01 -9.82776940e-01 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2b73bc69-bdb3-4f94-9d1b-645425317f16	reliability-adaptive-consistency	2303.05164	null	https://arxiv.org/abs/2303.05164v1	https://arxiv.org/pdf/2303.05164v1.pdf	Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation	Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores to apply the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations, which has not been well studied. We observe that the straightforward way of applying consistency constraints to weakly-supervised point cloud segmentation has two major limitations: noisy pseudo labels due to the conventional confidence-based selection and insufficient consistency constraints due to discarding unreliable pseudo labels. Therefore, we propose a novel Reliability-Adaptive Consistency Network (RAC-Net) to use both prediction confidence and model uncertainty to measure the reliability of pseudo labels and apply consistency training on all unlabeled points while with different consistency constraints for different points based on the reliability of corresponding pseudo labels. Experimental results on the S3DIS and ScanNet-v2 benchmark datasets show that our model achieves superior performance in weakly-supervised point cloud segmentation. The code will be released.	['Jianfei Cai', 'Guosheng Lin', 'Yicheng Wu', 'Zhonghua Wu']	2023-03-09	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[-1.54010832e-01 2.18779385e-01 -4.33019340e-01 -6.78279161e-01 -1.04771304e+00 -4.83902574e-01 1.81281373e-01 1.80811331e-01 -2.71054953e-01 5.25740027e-01 -5.07372797e-01 -1.98113233e-01 -1.59293205e-01 -3.54901910e-01 -8.79054606e-01 -6.95463598e-01 8.27624742e-03 1.20172691e+00 4.22949165e-01 2.45463967e-01 2.14510098e-01 5.41431129e-01 -1.57388687e+00 -1.33823082e-01 1.18081033e+00 1.02511668e+00 1.36640877e-01 1.32206902e-01 -3.01381111e-01 4.86064464e-01 -1.81890309e-01 -2.07321882e-01 5.73528409e-01 3.91712748e-02 -9.10790443e-01 4.67699707e-01 3.64080429e-01 -4.13722657e-02 4.91965771e-01 1.25015891e+00 2.82317717e-02 -2.70995703e-02 5.61419010e-01 -1.52348006e+00 -4.12739426e-01 3.70419204e-01 -8.19175363e-01 -2.90099919e-01 6.30524084e-02 -8.64909664e-02 9.51726317e-01 -1.03151047e+00 3.63866866e-01 9.07240689e-01 9.27504718e-01 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-9.03657138e-01 -6.60760045e-01 6.06358469e-01 1.95776209e-01 -7.93493569e-01 -5.31972528e-01 -1.26389503e-01]	[7.995454788208008, -3.218722105026245]
6e010a06-b2ba-463c-bee8-6da144acd03d	innovators-smm4h22-an-ensembles-approach-for-1	null	null	https://aclanthology.org/2022.smm4h-1.35	https://aclanthology.org/2022.smm4h-1.35.pdf	Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets	This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates related to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. The shared task emphasizes finding the level of cooperation within the mandates for their stance towards the health orders of the pandemic. Overall the shared subjects the participants to propose system’s that can efficiently perform 1) Stance Detection, which focuses on determining the author’s point of view in the text. 2) Premise Classification, which indicates whether or not the text has arguments. Through this paper we propose an orchestration of multiple transformer based encoders to derive the output for stance and premise classification. Our best model achieves a F1 score of 0.771 for Premise Classification and an aggregate macro-F1 score of 0.661 for Stance Detection. We have made our code public here	['Muskaan Singh', 'Nidhir Bhavsar', 'Aakash Bhatnagar', 'Vatsal Savaliya']	null	null	null	null	smm4h-coling-2022-10	['stance-detection']	['natural-language-processing']	[ 1.56682104e-01 7.09270239e-01 -3.29810560e-01 -5.15220404e-01 -9.62782443e-01 -5.07718623e-01 9.16005731e-01 7.46981323e-01 -3.28327328e-01 5.96786141e-01 8.11022460e-01 -8.89286220e-01 6.14138320e-02 -7.59685338e-01 -6.56201959e-01 -5.19859850e-01 6.52573407e-02 6.09875023e-01 -1.73930481e-01 -5.28731048e-01 9.00056437e-02 -4.82801318e-01 -1.06029701e+00 9.34320033e-01 3.62273604e-01 9.76133525e-01 -3.24889570e-01 7.75325537e-01 1.95566013e-01 1.30179822e+00 -8.99824083e-01 -5.19544244e-01 -3.16511720e-01 -3.42332393e-01 -1.08767307e+00 -5.95864952e-01 -3.38729732e-02 -2.71949410e-01 3.74159425e-01 7.68487275e-01 6.59119189e-01 -7.02558458e-01 6.55311882e-01 -1.30683732e+00 -2.01390997e-01 1.23431075e+00 -2.65617490e-01 5.97278774e-01 5.15252292e-01 1.52040767e-02 1.30641019e+00 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25ded87e-7a9a-48e5-ad51-17a42394a08e	universal-language-modelling-agent	2306.06521	null	https://arxiv.org/abs/2306.06521v1	https://arxiv.org/pdf/2306.06521v1.pdf	Universal Language Modelling agent	Large Language Models are designed to understand complex Human Language. Yet, Understanding of animal language has long intrigued researchers striving to bridge the communication gap between humans and other species. This research paper introduces a novel approach that draws inspiration from the linguistic concepts found in the Quran, a revealed Holy Arabic scripture dating back 1400 years. By exploring the linguistic structure of the Quran, specifically the components of ism, fil, and harf, we aim to unlock the underlying intentions and meanings embedded within animal conversations using audio data. To unravel the intricate complexities of animal language, we employ word embedding techniques to analyze each distinct frequency component. This methodology enables the identification of potential correlations and the extraction of meaningful insights from the data. Furthermore, we leverage a bioacoustics model to generate audio, which serves as a valuable resource for training natural language processing (NLP) techniques. This Paper aims to find the intention* behind animal language rather than having each word translation.	['Anees Aslam']	2023-06-10	null	null	null	null	['word-translation']	['natural-language-processing']	[ 2.80422926e-01 2.04214886e-01 3.24710347e-02 1.12260096e-02 -1.38237655e-01 -6.40108585e-01 6.40304863e-01 2.26196229e-01 -1.76981077e-01 3.16233158e-01 8.91924381e-01 -5.21377623e-01 2.10000034e-02 -6.24618590e-01 -2.96106994e-01 -5.59544921e-01 -3.67407113e-01 -1.23410285e-01 -3.34745795e-01 -6.84791744e-01 3.17435294e-01 2.55087346e-01 -1.40988207e+00 2.66505301e-01 4.58302587e-01 5.09381473e-01 3.70202899e-01 7.82332003e-01 -2.95904636e-01 1.12912083e+00 -4.82416511e-01 -3.42984557e-01 -2.57411808e-01 -8.73675406e-01 -8.19956899e-01 -1.72273919e-01 -5.60768694e-02 -3.91751491e-02 -3.28045487e-01 7.74501324e-01 2.11897120e-01 -5.62755242e-02 4.73173290e-01 -9.42130327e-01 -7.57048965e-01 1.04561484e+00 -1.64600551e-01 3.25733513e-01 4.32250887e-01 3.87425646e-02 1.62202442e+00 -7.27691650e-01 4.26230431e-01 1.43155873e+00 8.01350594e-01 6.00960493e-01 -1.21345174e+00 -4.11983192e-01 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1.90852582e-01 6.31704688e-01 -1.92196563e-01 -4.62059736e-01 5.59812605e-01]	[10.671309471130371, 9.28238582611084]
5f0be6ff-81a3-47bb-8dd9-7618b050bf4e	mbw-multi-view-bootstrapping-in-the-wild	2210.01721	null	https://arxiv.org/abs/2210.01721v1	https://arxiv.org/pdf/2210.01721v1.pdf	MBW: Multi-view Bootstrapping in the Wild	Labeling articulated objects in unconstrained settings have a wide variety of applications including entertainment, neuroscience, psychology, ethology, and many fields of medicine. Large offline labeled datasets do not exist for all but the most common articulated object categories (e.g., humans). Hand labeling these landmarks within a video sequence is a laborious task. Learned landmark detectors can help, but can be error-prone when trained from only a few examples. Multi-camera systems that train fine-grained detectors have shown significant promise in detecting such errors, allowing for self-supervised solutions that only need a small percentage of the video sequence to be hand-labeled. The approach, however, is based on calibrated cameras and rigid geometry, making it expensive, difficult to manage, and impractical in real-world scenarios. In this paper, we address these bottlenecks by combining a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark estimates from videos with only two or three uncalibrated, handheld cameras. With just a few annotations (representing 1-2% of the frames), we are able to produce 2D results comparable to state-of-the-art fully supervised methods, along with 3D reconstructions that are impossible with other existing approaches. Our Multi-view Bootstrapping in the Wild (MBW) approach demonstrates impressive results on standard human datasets, as well as tigers, cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos captured casually in a zoo. We release the codebase for MBW as well as this challenging zoo dataset consisting image frames of tail-end distribution categories with their corresponding 2D, 3D labels generated from minimal human intervention.	['Simon Lucey', 'Ian R. Fasel', 'Laszlo Attila Jeni', 'Tim Clifford', 'Chaoyang Wang', 'Mosam Dabhi']	2022-10-04	null	null	null	null	['unsupervised-landmark-detection', 'semi-supervised-2d-and-3d-landmark-labeling']	['computer-vision', 'computer-vision']	[-1.54413581e-02 -5.63994087e-02 7.39610791e-02 -2.69714624e-01 -7.01741278e-01 -8.08996141e-01 2.88509369e-01 -2.22023293e-01 -7.73893178e-01 7.33850479e-01 -1.46287590e-01 1.64100736e-01 2.65777618e-01 -2.35569254e-01 -7.94706702e-01 -4.53487605e-01 -2.74452180e-01 8.98273706e-01 5.38888991e-01 9.73774418e-02 -8.03314894e-03 5.33457935e-01 -1.55113041e+00 -5.70723154e-02 1.90565556e-01 6.70206249e-01 3.29619825e-01 5.87024212e-01 2.74299502e-01 4.72437412e-01 -1.65669501e-01 -4.86500025e-01 4.93660957e-01 -2.44865090e-01 -7.41887212e-01 2.46030539e-01 9.34329331e-01 -8.42470527e-01 -1.35387585e-01 8.31674516e-01 4.59402829e-01 2.32164517e-01 4.52826619e-01 -1.15926838e+00 -9.77821127e-02 2.64190137e-01 -7.86852717e-01 1.29051730e-01 5.32687485e-01 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8ee2f3a3-5eba-4c79-901e-31bb456000be	deep-generative-models-on-3d-representations	2210.15663	null	https://arxiv.org/abs/2210.15663v2	https://arxiv.org/pdf/2210.15663v2.pdf	Deep Generative Models on 3D Representations: A Survey	Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances. Along with the rise of neural networks, deep generative models, such as variational autoencoders (VAEs) and generative adversarial network (GANs), have made tremendous progress in 2D image synthesis. Recently, researchers switch their attentions from the 2D space to the 3D space considering that 3D data better aligns with our physical world and hence enjoys great potential in practice. However, unlike a 2D image, which owns an efficient representation (i.e., pixel grid) by nature, representing 3D data could face far more challenges. Concretely, we would expect an ideal 3D representation to be capable enough to model shapes and appearances in details, and to be highly efficient so as to model high-resolution data with fast speed and low memory cost. However, existing 3D representations, such as point clouds, meshes, and recent neural fields, usually fail to meet the above requirements simultaneously. In this survey, we make a thorough review of the development of 3D generation, including 3D shape generation and 3D-aware image synthesis, from the perspectives of both algorithms and more importantly representations. We hope that our discussion could help the community track the evolution of this field and further spark some innovative ideas to advance this challenging task.	['Yujun Shen', 'Yiyi Liao', 'Yinghao Xu', 'Sida Peng', 'Zifan Shi']	2022-10-27	null	null	null	null	['3d-shape-generation', '3d-aware-image-synthesis']	['computer-vision', 'computer-vision']	[ 1.71800196e-01 2.29685888e-01 2.34655868e-02 -2.47564651e-02 -4.00336921e-01 -4.61992532e-01 8.83912861e-01 -3.51882398e-01 2.89646834e-01 7.44195759e-01 8.12512860e-02 -1.34771839e-01 1.75910920e-01 -1.44448566e+00 -8.12007725e-01 -8.93174291e-01 2.39536926e-01 5.82276165e-01 -1.18092522e-01 -2.67298430e-01 2.21337173e-02 8.98596466e-01 -1.64376950e+00 -1.89629793e-01 8.78125966e-01 9.79071677e-01 1.05208047e-01 3.53543580e-01 -4.39658046e-01 2.93874502e-01 -5.41291595e-01 -4.37577933e-01 1.91722855e-01 -5.84068239e-01 -3.81100029e-01 2.55565673e-01 2.85898954e-01 -4.65996973e-02 -6.14051878e-01 9.81345177e-01 4.71626043e-01 1.24273293e-01 9.16478515e-01 -1.30287933e+00 -1.18107939e+00 3.05054691e-02 -5.11487722e-01 -4.69937265e-01 2.78840289e-02 1.69359118e-01 5.70211887e-01 -8.40941489e-01 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5.40882051e-01 7.37856269e-01 -1.28097224e+00 -3.77270937e-01 -7.31468320e-01 -6.78215325e-01 3.13322008e-01 8.79782438e-02 -9.62525725e-01 -1.76809043e-01 -2.30546929e-02]	[9.01052188873291, -3.546456813812256]
28e6296f-46eb-4ce9-b32a-8ed5d7940b37	an-empirical-evaluation-of-generic	1803.01271	null	http://arxiv.org/abs/1803.01271v2	http://arxiv.org/pdf/1803.01271v2.pdf	An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling	For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at http://github.com/locuslab/TCN .	['Shaojie Bai', 'Vladlen Koltun', 'J. Zico Kolter']	2018-03-04	null	null	null	null	['sequential-image-classification', 'music-modeling']	['computer-vision', 'music']	[ 4.45867002e-01 -7.10597932e-02 -3.34969580e-01 -2.21075475e-01 -5.45019746e-01 -5.35275519e-01 6.00234628e-01 -3.16423118e-01 -4.26037997e-01 5.12218297e-01 4.53041971e-01 -7.90707111e-01 4.05877143e-01 -3.12125534e-01 -7.21512258e-01 -3.35721344e-01 3.65671590e-02 1.01444989e-01 -7.87797272e-02 -2.77687490e-01 1.38887867e-01 3.80173028e-01 -1.34699929e+00 3.73828560e-01 2.82037705e-01 6.42327785e-01 3.93182755e-01 7.54349172e-01 -1.78714618e-01 1.16223001e+00 -5.64264774e-01 -4.49746996e-01 -9.65909362e-02 -5.93339741e-01 -1.09897792e+00 -2.70103365e-01 -3.88043635e-02 -1.83630317e-01 -6.01266980e-01 7.60132015e-01 8.00382793e-01 3.14521432e-01 2.12335601e-01 -8.98128390e-01 -6.18573010e-01 1.03976214e+00 -1.13049723e-01 4.14027303e-01 2.32609943e-01 3.05495560e-01 1.13880372e+00 -8.58152926e-01 6.04658782e-01 1.12607479e+00 1.06462002e+00 7.30157256e-01 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-1.24301696e+00 -2.75086313e-01 -1.49917737e-01 3.78928751e-01 -3.20776284e-01 2.10888907e-02 9.37092006e-02]	[10.84047794342041, 6.832894802093506]
d68d3532-8a7b-4048-8ae1-829fc35551a7	survey-of-matrix-completion-algorithms	2204.01532	null	https://arxiv.org/abs/2204.01532v2	https://arxiv.org/pdf/2204.01532v2.pdf	Survey of Matrix Completion Algorithms	Matrix completion problem has been investigated under many different conditions since Netflix announced the Netflix Prize problem. Many research work has been done in the field once it has been discovered that many real life dataset could be estimated with a low-rank matrix. Since then compressed sensing, adaptive signal detection has gained the attention of many researchers. In this survey paper we are going to visit some of the matrix completion methods, mainly in the direction of passive and adaptive directions. First, we discuss passive matrix completion methods with convex optimization, and the second active matrix completion techniques with adaptive signal detection methods. Traditionally many machine learning problems are solved in passive environment. However, later it has been observed that adaptive sensing algorithms many times performs more efficiently than former algorithms. Hence algorithms in this setting has been extensively studied. Therefore, we are going to present some of the latest adaptive matrix completion algorithms in this paper meanwhile providing passive methods.	['Jafar Jafarov']	2022-04-01	null	null	null	null	['matrix-completion']	['methodology']	[ 1.02647424e+00 -2.76654363e-02 -2.85130814e-02 -9.03680399e-02 -7.50146627e-01 -5.25114536e-01 3.03196669e-01 -5.57724126e-02 -4.90052968e-01 5.65331399e-01 4.93362933e-01 -6.89842552e-02 -5.94882607e-01 -3.31166267e-01 -4.41424072e-01 -8.73522401e-01 -5.29242337e-01 1.37274474e-01 -2.94818848e-01 -3.19610387e-01 2.75715739e-01 1.79048210e-01 -1.01487148e+00 5.85031556e-03 6.20319605e-01 8.24838281e-01 4.27795231e-01 8.31895053e-01 3.86998296e-01 7.36564457e-01 -1.98964864e-01 -2.73202121e-01 9.07272816e-01 -3.62146467e-01 -3.41911972e-01 4.99354750e-01 2.21477225e-01 2.79716440e-02 -5.92888296e-01 1.34265304e+00 7.52729177e-01 4.34112549e-01 1.48412496e-01 -1.20502388e+00 -3.78102273e-01 8.81515324e-01 -9.61214662e-01 3.15294504e-01 7.32303262e-01 -5.33189714e-01 9.17182922e-01 -1.37728417e+00 3.24609548e-01 1.11438704e+00 7.25034535e-01 3.70456763e-02 -1.12865913e+00 -5.87666154e-01 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2.38076463e-01 -3.89188945e-01 -3.81128937e-01 1.51963860e-01]	[6.988105773925781, 4.6638689041137695]
e81610e6-8c91-4aec-bb99-0ca47fe72b47	causal-models-in-string-diagrams	2304.07638	null	https://arxiv.org/abs/2304.07638v1	https://arxiv.org/pdf/2304.07638v1.pdf	Causal models in string diagrams	The framework of causal models provides a principled approach to causal reasoning, applied today across many scientific domains. Here we present this framework in the language of string diagrams, interpreted formally using category theory. A class of string diagrams, called network diagrams, are in 1-to-1 correspondence with directed acyclic graphs. A causal model is given by such a diagram with its components interpreted as stochastic maps, functions, or general channels in a symmetric monoidal category with a 'copy-discard' structure (cd-category), turning a model into a single mathematical object that can be reasoned with intuitively and yet rigorously. Building on prior works by Fong and Jacobs, Kissinger and Zanasi, as well as Fritz and Klingler, we present diagrammatic definitions of causal models and functional causal models in a cd-category, generalising causal Bayesian networks and structural causal models, respectively. We formalise general interventions on a model, including but beyond do-interventions, and present the natural notion of an open causal model with inputs. We also give an approach to conditioning based on a normalisation box, allowing for causal inference calculations to be done fully diagrammatically. We define counterfactuals in this setup, and treat the problems of the identifiability of causal effects and counterfactuals fully diagrammatically. The benefits of such a presentation of causal models lie in foundational questions in causal reasoning and in their clarificatory role and pedagogical value. This work aims to be accessible to different communities, from causal model practitioners to researchers in applied category theory, and discusses many examples from the literature for illustration. Overall, we argue and demonstrate that causal reasoning according to the causal model framework is most naturally and intuitively done as diagrammatic reasoning.	['Sean Tull', 'Robin Lorenz']	2023-04-15	null	null	null	null	['causal-inference', 'causal-inference']	['knowledge-base', 'miscellaneous']	[ 5.06287873e-01 7.64668465e-01 -4.08605009e-01 -2.33747691e-01 1.54620484e-01 -7.34713554e-01 1.45206988e+00 4.65476662e-02 9.72031578e-02 6.92545176e-01 8.16406608e-01 -1.04528821e+00 -1.04956174e+00 -1.16777384e+00 -8.63906741e-01 -6.41022444e-01 -6.82803750e-01 2.55082399e-01 9.60843340e-02 -1.14266485e-01 3.14997643e-01 3.12059850e-01 -1.44852996e+00 6.88336864e-02 6.94153070e-01 2.46037439e-01 -1.90469921e-01 7.36779273e-01 -1.92408524e-02 1.11418533e+00 -1.15450479e-01 -6.82765365e-01 -2.69071490e-01 -7.84850478e-01 -1.12484968e+00 -3.90196383e-01 3.83517705e-03 -6.15482815e-02 -5.33872962e-01 8.40735018e-01 -7.02174529e-02 -1.82128772e-01 1.04580534e+00 -1.45114803e+00 -8.01373363e-01 1.35757864e+00 -3.38089406e-01 4.22639325e-02 5.73204041e-01 -1.28327742e-01 1.23310947e+00 -4.99162436e-01 5.40851235e-01 2.06652308e+00 4.38796610e-01 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ce5d8db2-b8ac-4eb7-bb88-c8cb5ef824ec	distributed-adversarial-training-to-robustify-1	2206.06257	null	https://arxiv.org/abs/2206.06257v2	https://arxiv.org/pdf/2206.06257v2.pdf	Distributed Adversarial Training to Robustify Deep Neural Networks at Scale	Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach, known as adversarial training (AT), has been shown to mitigate the negative impact of adversarial attacks by virtue of a min-max robust training method. While effective, it remains unclear whether it can successfully be adapted to the distributed learning context. The power of distributed optimization over multiple machines enables us to scale up robust training over large models and datasets. Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines. We show that DAT is general, which supports training over labeled and unlabeled data, multiple types of attack generation methods, and gradient compression operations favored for distributed optimization. Theoretically, we provide, under standard conditions in the optimization theory, the convergence rate of DAT to the first-order stationary points in general non-convex settings. Empirically, we demonstrate that DAT either matches or outperforms state-of-the-art robust accuracies and achieves a graceful training speedup (e.g., on ResNet-50 under ImageNet). Codes are available at https://github.com/dat-2022/dat.	['Sijia Liu', 'Mingyi Hong', 'Lior Horesh', 'Lee Martie', 'Quanfu Fan', 'Pin-Yu Chen', 'Xiangyi Chen', 'Yihua Zhang', 'Songtao Lu', 'Gaoyuan Zhang']	2022-06-13	distributed-adversarial-training-to-robustify	https://openreview.net/forum?id=kmBFHJ5pr0o	https://openreview.net/pdf?id=kmBFHJ5pr0o	null	['distributed-optimization']	['methodology']	[ 8.14477727e-02 -4.49745264e-03 1.11712657e-01 -3.76883209e-01 -1.01896250e+00 -1.02082109e+00 5.21327019e-01 -1.90084912e-02 -7.28832960e-01 6.98033929e-01 -2.61223644e-01 -6.22986615e-01 4.00198903e-03 -8.12390268e-01 -1.27565706e+00 -9.33506727e-01 -3.44255924e-01 3.36699247e-01 2.58459784e-02 -2.19425902e-01 -7.36661777e-02 9.34393585e-01 -9.80339646e-01 1.37734830e-01 5.16770303e-01 8.63080978e-01 -3.47042143e-01 1.01671541e+00 2.92879581e-01 7.59034991e-01 -1.06124699e+00 -8.53436172e-01 8.49974811e-01 -1.59722537e-01 -5.59703410e-01 -4.08550948e-01 8.83076012e-01 -6.60229146e-01 -6.65826559e-01 1.56388557e+00 7.64455676e-01 1.20605439e-01 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745df7b0-6c00-4875-8514-c1cf81ed2bc2	the-provable-benefits-of-unsupervised-data	2302.13493	null	https://arxiv.org/abs/2302.13493v1	https://arxiv.org/pdf/2302.13493v1.pdf	The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning	Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations. However, the question of how to conduct self-supervised offline reinforcement learning (RL) in a principled way remains unclear. In this paper, we address this issue by investigating the theoretical benefits of utilizing reward-free data in linear Markov Decision Processes (MDPs) within a semi-supervised setting. Further, we propose a novel, Provable Data Sharing algorithm (PDS) to utilize such reward-free data for offline RL. PDS uses additional penalties on the reward function learned from labeled data to prevent overestimation, ensuring a conservative algorithm. Our results on various offline RL tasks demonstrate that PDS significantly improves the performance of offline RL algorithms with reward-free data. Overall, our work provides a promising approach to leveraging the benefits of unlabeled data in offline RL while maintaining theoretical guarantees. We believe our findings will contribute to developing more robust self-supervised RL methods.	['Chongjie Zhang', 'Qianchuan Zhao', 'Yiqin Yang', 'Hao Hu']	2023-02-27	null	null	null	null	['offline-rl']	['playing-games']	[ 2.53687426e-02 4.57357913e-01 -7.75010645e-01 -6.53212905e-01 -1.19299138e+00 -6.24903738e-01 3.17766756e-01 8.88766870e-02 -7.70656407e-01 1.16599095e+00 8.29015598e-02 -3.56897980e-01 1.20445386e-01 -3.80743444e-01 -8.69035065e-01 -7.06710279e-01 -3.47143084e-01 3.89498502e-01 -6.46317005e-02 2.06176698e-01 -2.68761646e-02 3.50262970e-01 -1.35764563e+00 -1.69912100e-01 8.48085642e-01 1.01637018e+00 2.51391917e-01 5.16815305e-01 2.34403074e-01 1.32850718e+00 -3.21020663e-01 -1.45877570e-01 5.14644146e-01 -6.11545205e-01 -8.29900742e-01 1.39215618e-01 -1.54455118e-02 -1.00892866e+00 -2.97836095e-01 7.32012868e-01 4.61097032e-01 3.34534734e-01 2.48425126e-01 -1.47650480e+00 -3.58109742e-01 1.00578737e+00 -5.22025585e-01 9.91692301e-03 -7.30089098e-02 3.23545128e-01 1.18151367e+00 -2.71185935e-01 5.31879365e-01 1.08787060e+00 3.79629076e-01 9.88302767e-01 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-7.44608998e-01 1.31977210e-02 4.82829660e-01 -6.45288825e-01 -4.76817042e-01 -1.52391493e-02]	[4.080907344818115, 2.1831188201904297]
0a31368c-ee4c-4d67-a2a7-28ae4a2bbece	light-weighted-cnn-for-text-classification	2004.07922	null	https://arxiv.org/abs/2004.07922v1	https://arxiv.org/pdf/2004.07922v1.pdf	Light-Weighted CNN for Text Classification	For management, documents are categorized into a specific category, and to do these, most of the organizations use manual labor. In today's automation era, manual efforts on such a task are not justified, and to avoid this, we have so many software out there in the market. However, efficiency and minimal resource consumption is the focal point which is also creating a competition. The categorization of such documents into specified classes by machine provides excellent help. One of categorization technique is text classification using a Convolutional neural network(TextCNN). TextCNN uses multiple sizes of filters, as in the case of the inception layer introduced in Googlenet. The network provides good accuracy but causes high memory consumption due to a large number of trainable parameters. As a solution to this problem, we introduced a whole new architecture based on separable convolution. The idea of separable convolution already exists in the field of image classification but not yet introduces to text classification tasks. With the help of this architecture, we can achieve a drastic reduction in trainable parameters.	['Ritu Yadav']	2020-04-16	null	null	null	null	['document-image-classification']	['computer-vision']	[-0.04988106 -0.18714315 0.10613034 -0.59252 0.2420434 -0.45964152 0.59907776 0.17518361 -0.72071356 0.36096328 -0.2898486 -0.50386333 -0.11041854 -1.1624225 -0.19068234 -0.41870746 0.45290148 0.3257912 0.24727085 -0.27794665 0.6544092 0.42256984 -1.6222981 0.54902935 0.79350823 1.207264 0.6003436 0.4355845 -1.0699377 0.69916135 -0.70519286 -0.41649702 0.12582466 -0.07035264 -0.9726545 0.07428202 0.07559324 -0.38527566 -0.1368042 1.0725273 0.27312213 0.08046602 0.6379885 -1.07626 -0.7656959 0.7840067 -0.29327554 -0.00835047 -0.08727816 -0.28305668 0.7084921 -0.69000316 0.2819869 1.0867956 0.67581624 0.3795874 -0.88724256 -0.58822846 -0.02075949 0.12813796 -1.21903 -0.04400229 0.4318771 -0.7299582 0.91929716 0.2386455 0.5080511 0.8834432 0.44094437 0.4892662 0.8279203 -0.6039934 0.04431842 0.7335786 0.66851765 0.56630075 0.45768234 -0.58674395 -0.06078609 0.23591636 0.5693342 0.6898908 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97fabfe8-8e15-469c-a09b-db1da18611ef	transfer-learning-with-joint-fine-tuning-for	2210.05790	null	https://arxiv.org/abs/2210.05790v1	https://arxiv.org/pdf/2210.05790v1.pdf	Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis	Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current studies show that exploring other modalities (e.g., images) increases sentiment analysis performance. State-of-the-art multimodal models, such as CLIP and VisualBERT, are pre-trained on datasets with the text paired with images. Although the results obtained by these models are promising, pre-training and sentiment analysis fine-tuning tasks of these models are computationally expensive. This paper introduces a transfer learning approach using joint fine-tuning for sentiment analysis. Our proposal achieved competitive results using a more straightforward alternative fine-tuning strategy that leverages different pre-trained unimodal models and efficiently combines them in a multimodal space. Moreover, our proposal allows flexibility when incorporating any pre-trained model for texts and images during the joint fine-tuning stage, being especially interesting for sentiment classification in low-resource scenarios.	['Ricardo Marcondes Marcacini', 'Guilherme Lourenço de Toledo']	2022-10-11	null	null	null	null	['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis']	['computer-vision', 'natural-language-processing']	[ 6.79294243e-02 -1.95163548e-01 -2.16144443e-01 -4.88439023e-01 -9.34840560e-01 -8.99872661e-01 9.21597004e-01 8.44494551e-02 -7.97619581e-01 4.34490025e-01 3.55980277e-01 -7.62599707e-02 3.56783688e-01 -5.36810637e-01 -7.27819204e-01 -6.79968238e-01 3.06345284e-01 3.66830856e-01 3.74521166e-02 -4.60478276e-01 2.01025873e-01 8.54495242e-02 -1.66020787e+00 9.99533832e-01 3.22643876e-01 1.02720642e+00 7.44991079e-02 8.68999481e-01 -3.43376249e-01 6.61596656e-01 -3.62926394e-01 -1.03046966e+00 1.28138736e-01 -1.20406114e-01 -7.67031252e-01 2.02651635e-01 6.70819461e-01 -2.96267241e-01 -5.33134378e-02 8.49669933e-01 5.86800039e-01 8.10387209e-02 7.27375746e-01 -1.32557583e+00 -7.07023442e-01 5.22453785e-01 -8.76854181e-01 -1.58777267e-01 4.90891576e-01 -1.00217890e-02 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1438ff8b-8ae2-4b9e-a56a-e31cc681d0fa	a-threefold-review-on-deep-semantic	2303.04315	null	https://arxiv.org/abs/2303.04315v1	https://arxiv.org/pdf/2303.04315v1.pdf	A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design	Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Networks, have boosted semantic segmentation to a new level in terms of performance and generalization capabilities. However, designing Deep Semantic Segmentation models is a complex task, as it may involve application-dependent aspects. Particularly, when considering autonomous driving applications, the robustness-efficiency trade-off, as well as intrinsic limitations - computational/memory bounds and data-scarcity - and constraints - real-time inference - should be taken into consideration. In this respect, the use of additional data modalities, such as depth perception for reasoning on the geometry of a scene, and temporal cues from videos to explore redundancy and consistency, are promising directions yet not explored to their full potential in the literature. In this paper, we conduct a survey on the most relevant and recent advances in Deep Semantic Segmentation in the context of vision for autonomous vehicles, from three different perspectives: efficiency-oriented model development for real-time operation, RGB-Depth data integration (RGB-D semantic segmentation), and the use of temporal information from videos in temporally-aware models. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective, so that the reader can not only get started, but also be up to date in respect to recent advances in this exciting and challenging research field.	['Fernando Santos Osório', 'Felipe Manfio Barbosa']	2023-03-08	null	null	null	null	['video-semantic-segmentation', 'data-integration']	['computer-vision', 'knowledge-base']	[ 4.12238210e-01 -1.66617081e-01 -1.82921529e-01 -4.78526533e-01 -2.21929684e-01 -4.47072566e-01 4.33133930e-01 7.79635981e-02 -5.80536544e-01 4.25861835e-01 -4.81276244e-01 -1.23178571e-01 -3.35689992e-01 -9.11731839e-01 -4.84083235e-01 -8.56071472e-01 2.49637682e-02 4.06340182e-01 5.08814275e-01 -2.09708601e-01 3.74643356e-01 8.38517964e-01 -2.16716099e+00 -1.09421238e-01 8.46948326e-01 1.36578274e+00 5.54547906e-01 3.56320649e-01 -2.55235136e-01 4.43579346e-01 -2.65020490e-01 -2.50258565e-01 1.48996934e-01 -2.76343852e-01 -6.23613715e-01 4.51208830e-01 7.61952847e-02 -2.03100771e-01 -2.96734601e-01 1.02996361e+00 1.66882440e-01 2.84471452e-01 2.86181360e-01 -1.19838119e+00 -1.04617998e-02 -4.09423038e-02 -3.09924692e-01 2.87234187e-01 -1.87822711e-02 3.27397704e-01 6.42536879e-01 -5.86945355e-01 5.96892595e-01 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ffe9c755-7e38-4468-9e1b-f0ba90bedaf2	temporal-word-meaning-disambiguation-using	2210.08207	null	https://arxiv.org/abs/2210.08207v2	https://arxiv.org/pdf/2210.08207v2.pdf	Temporal Word Meaning Disambiguation using TimeLMs	Meaning of words constantly changes given the events in modern civilization. Large Language Models use word embeddings, which are often static and thus cannot cope with this semantic change. Thus,it is important to resolve ambiguity in word meanings. This paper is an effort in this direction, where we explore methods for word sense disambiguation for the EvoNLP shared task. We conduct rigorous ablations for two solutions to this problem. We see that an approach using time-aware language models helps this task. Furthermore, we explore possible future directions to this problem.	['Aditya Kane', 'Parth Dandavate', 'Mihir Godbole']	2022-10-15	null	null	null	null	['word-sense-disambiguation']	['natural-language-processing']	[-5.22032343e-02 -1.33744121e-01 -3.77133965e-01 -4.09091920e-01 -4.67410058e-01 -8.07814717e-01 7.26750135e-01 4.99063462e-01 -9.42995071e-01 6.40052319e-01 6.85050905e-01 -5.22139311e-01 -1.87901467e-01 -8.64643037e-01 4.46636900e-02 -3.68268043e-01 -2.23775402e-01 4.10124481e-01 1.78422183e-01 -6.67097747e-01 5.51448047e-01 2.97640204e-01 -1.07251894e+00 -8.93563256e-02 4.24950391e-01 2.57520109e-01 1.89202473e-01 6.11827672e-01 -7.61151552e-01 2.89392084e-01 -7.62512088e-01 -2.98568130e-01 6.00076504e-02 7.85525665e-02 -1.15129113e+00 -6.02872550e-01 1.94812194e-01 2.58278131e-01 -2.12445542e-01 1.04697573e+00 5.89900136e-01 5.96514881e-01 4.19971496e-01 -1.23800063e+00 -9.83900070e-01 8.69834542e-01 -4.65576798e-01 8.88907790e-01 3.19605440e-01 -1.77450657e-01 1.34916091e+00 -8.60798836e-01 6.06628835e-01 1.48496830e+00 6.85849547e-01 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c3ad5947-9c79-4bca-a3b8-87adbcaaa267	real-time-online-skeleton-extraction-and	2206.11376	null	https://arxiv.org/abs/2206.11376v1	https://arxiv.org/pdf/2206.11376v1.pdf	Real-Time Online Skeleton Extraction and Gesture Recognition on Pepper	We present a multi-stage pipeline for simple gesture recognition. The novelty of our approach is the association of different technologies, resulting in the first real-time system as of now to conjointly extract skeletons and recognise gesture on a Pepper robot. For this task, Pepper has been augmented with an embedded GPU for running deep CNNs and a fish-eye camera to capture whole scene interaction. We show in this article that real-case scenarios are challenging, and the state-of-the-art approaches hardly deal with unknown human gestures. We present here a way to handle such cases.	['Jean-Marc Montanier', 'Axel Lefrant']	2022-06-22	null	null	null	null	['gesture-recognition']	['computer-vision']	[ 6.67006299e-02 8.36847797e-02 4.25869167e-01 -1.30806088e-01 -2.79942930e-01 -4.34003621e-01 5.69778681e-01 -4.13462132e-01 -8.41869473e-01 1.46395728e-01 -1.53760731e-01 5.45773916e-02 1.61528543e-01 -3.72299939e-01 -4.98614371e-01 -4.96297598e-01 -1.87846884e-01 6.27882659e-01 8.44549417e-01 -3.80216002e-01 2.15111107e-01 9.49413180e-01 -1.55503333e+00 2.87331879e-01 -1.74394131e-01 6.94815040e-01 2.16524422e-01 1.26046276e+00 2.14611236e-02 4.65427935e-01 -4.48174447e-01 -4.78889912e-01 6.30079865e-01 -2.42399111e-01 -6.58824086e-01 7.41403848e-02 3.30367893e-01 -7.37894654e-01 -5.78453660e-01 5.17845154e-01 6.81210876e-01 -5.82938753e-02 5.22404850e-01 -1.25930059e+00 2.77811915e-01 3.64153922e-01 -4.03985441e-01 -2.90515184e-01 5.65109670e-01 4.20320302e-01 4.65950191e-01 -6.59079432e-01 7.82766640e-01 1.16674328e+00 7.44607806e-01 8.07454050e-01 -6.31486535e-01 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e98dfc82-1f80-4ee1-ad23-1b8740ae7def	dasnet-dual-attentive-fully-convolutional	2003.03608	null	https://arxiv.org/abs/2003.03608v2	https://arxiv.org/pdf/2003.03608v2.pdf	DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images	Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance of current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet) for change detection in high-resolution images. Through the dual-attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e. unchanged samples are much more than changed samples, which is one of the main reasons resulting in pseudo-changes. We put forward the weighted double margin contrastive loss to address this problem by punishing the attention to unchanged feature pairs and increase attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.1\% and 3.6\%, respectively, in the F1 score. Our Pytorch implementation is available at https://github.com/lehaifeng/DASNet.	['Yu Liu', 'Haifeng Li', 'Jie Chen', 'Li Chen', 'Haozhe Huang', 'Ziyang Yuan', 'Jiawei Zhu', 'Jian Peng']	2020-03-07	null	null	null	null	['change-detection-for-remote-sensing-images']	['miscellaneous']	[ 2.58538604e-01 -6.18592978e-01 2.08507732e-01 -4.71227974e-01 -4.75217849e-01 -1.84482232e-01 4.66139525e-01 -1.55140534e-01 -4.81375158e-01 6.24956250e-01 1.55994758e-01 8.74813199e-02 -2.01745152e-01 -9.47859764e-01 -6.00985408e-01 -9.76851463e-01 7.83548057e-02 -2.03124493e-01 2.02797413e-01 -4.36194599e-01 1.28684536e-01 6.05034471e-01 -1.62694037e+00 6.74056411e-02 1.11447978e+00 9.55444694e-01 3.29471022e-01 2.59825349e-01 1.45036560e-02 4.51793432e-01 -2.64326930e-01 2.61414677e-01 4.69700336e-01 -4.24359232e-01 -4.50328976e-01 -3.36366780e-02 4.30803716e-01 -5.26605606e-01 -4.80848372e-01 1.38594425e+00 8.45609546e-01 2.35414818e-01 3.44136238e-01 -9.93531346e-01 -7.10286856e-01 4.46664542e-01 -9.64659274e-01 8.86121631e-01 -1.99925348e-01 2.26327941e-01 9.62055266e-01 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489c51de-0c0d-4ce5-b7a2-04accd6e4e58	a-resource-efficient-embedded-iris	1909.03385	null	https://arxiv.org/abs/1909.03385v1	https://arxiv.org/pdf/1909.03385v1.pdf	A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks	Applications of Fully Convolutional Networks (FCN) in iris segmentation have shown promising advances. For mobile and embedded systems, a significant challenge is that the proposed FCN architectures are extremely computationally demanding. In this article, we propose a resource-efficient, end-to-end iris recognition flow, which consists of FCN-based segmentation, contour fitting, followed by Daugman normalization and encoding. To attain accurate and efficient FCN models, we propose a three-step SW/HW co-design methodology consisting of FCN architectural exploration, precision quantization, and hardware acceleration. In our exploration, we propose multiple FCN models, and in comparison to previous works, our best-performing model requires 50X less FLOPs per inference while achieving a new state-of-the-art segmentation accuracy. Next, we select the most efficient set of models and further reduce their computational complexity through weights and activations quantization using 8-bit dynamic fixed-point (DFP) format. Each model is then incorporated into an end-to-end flow for true recognition performance evaluation. A few of our end-to-end pipelines outperform the previous state-of-the-art on two datasets evaluated. Finally, we propose a novel DFP accelerator and fully demonstrate the SW/HW co-design realization of our flow on an embedded FPGA platform. In comparison with the embedded CPU, our hardware acceleration achieves up to 8.3X speedup for the overall pipeline while using less than 15% of the available FPGA resources. We also provide comparisons between the FPGA system and an embedded GPU showing different benefits and drawbacks for the two platforms.	['Hokchhay Tann', 'Sherief Reda', 'Heng Zhao']	2019-09-08	null	null	null	null	['iris-segmentation']	['medical']	[ 3.75372291e-01 -2.93762296e-01 -3.47208142e-01 -3.57283622e-01 -2.38354191e-01 -4.08995986e-01 1.34095341e-01 3.05912327e-02 -7.05894887e-01 1.55332938e-01 -8.60532820e-02 -7.80796409e-01 -2.51068711e-01 -5.62919319e-01 -5.04624367e-01 -3.50143671e-01 1.14754401e-01 3.34848836e-02 3.72637138e-02 -1.72100868e-02 4.69745964e-01 7.73615062e-01 -1.71870708e+00 2.34092087e-01 9.77182925e-01 1.12319756e+00 -3.52307081e-01 1.12602508e+00 4.13559116e-02 5.78958213e-01 -6.04857981e-01 -5.41111290e-01 5.48480809e-01 -2.50190675e-01 -6.22520447e-01 -6.60336912e-02 9.71410334e-01 -4.69312429e-01 -1.53670698e-01 1.02839577e+00 7.38745272e-01 -1.84311867e-01 4.03286368e-02 -8.03955853e-01 -9.15626809e-02 3.85303974e-01 -8.56215000e-01 2.34038308e-01 -7.81518444e-02 4.46170211e-01 4.11475688e-01 -4.63785708e-01 3.01642597e-01 1.14765799e+00 9.15617883e-01 4.87764359e-01 -9.97498214e-01 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-3.94811690e-01 4.29927617e-01 -1.78882256e-01 -4.69367839e-02 -1.83906257e-01]	[8.597228050231934, 2.824005126953125]
941ee1c7-4eba-46dc-9ca7-14ee4446a225	diachronic-word-embeddings-reveal-statistical	1605.09096	null	http://arxiv.org/abs/1605.09096v6	http://arxiv.org/pdf/1605.09096v6.pdf	Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change	Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test. Word embeddings show promise as a diachronic tool, but have not been carefully evaluated. We develop a robust methodology for quantifying semantic change by evaluating word embeddings (PPMI, SVD, word2vec) against known historical changes. We then use this methodology to reveal statistical laws of semantic evolution. Using six historical corpora spanning four languages and two centuries, we propose two quantitative laws of semantic change: (i) the law of conformity---the rate of semantic change scales with an inverse power-law of word frequency; (ii) the law of innovation---independent of frequency, words that are more polysemous have higher rates of semantic change.	['Dan Jurafsky', 'William L. Hamilton', 'Jure Leskovec']	2016-05-30	diachronic-word-embeddings-reveal-statistical-1	https://aclanthology.org/P16-1141	https://aclanthology.org/P16-1141.pdf	acl-2016-8	['diachronic-word-embeddings']	['natural-language-processing']	[-1.02440529e-01 -4.11008537e-01 -3.01082462e-01 -1.93159029e-01 6.82402216e-03 -8.44293296e-01 1.20973074e+00 5.52919209e-01 -9.23751712e-01 6.02201283e-01 9.25535142e-01 -6.04660153e-01 -4.06437889e-02 -9.54302788e-01 -4.37327534e-01 -4.22502011e-01 -6.82781562e-02 1.85286999e-01 2.04389617e-01 -6.22919798e-01 5.04858553e-01 7.31312037e-02 -1.29123271e+00 -4.79387790e-01 6.54687226e-01 3.84912640e-01 1.13178320e-01 5.70850968e-01 -3.93710673e-01 1.31839782e-01 -4.64531302e-01 -5.17364025e-01 1.51699424e-01 -6.41267836e-01 -8.36653292e-01 -4.19313759e-01 1.25461802e-01 3.25989187e-01 -6.27546191e-01 1.32369471e+00 3.12349528e-01 2.18390808e-01 8.64392996e-01 -6.45916879e-01 -1.55590212e+00 8.25320780e-01 -2.87678957e-01 8.41950297e-01 2.17302799e-01 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799dc09c-75e2-4e21-bace-1d073776789b	auco-resnet-an-end-to-end-network-for-covid	null	null	https://www.sciencedirect.com/science/article/pii/S0031320322001376	https://www.researchgate.net/publication/359245461_AUCO_ResNet_an_end-to-end_network_for_Covid-19_pre-screening_from_cough_and_breath	AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath	This study presents the Auditory Cortex ResNet (AUCO ResNet), it is a biologically inspired deep neural network especially designed for sound classification and more specifically for Covid-19 recognition from audio tracks of coughs and breaths. Differently from other approaches, it can be trained end-to-end thus optimizing (with gradient descent) all the modules of the learning algorithm: mel-like filter design, feature extraction, feature selection, dimensionality reduction and prediction. This neural network includes three attention mechanisms namely the squeeze and excitation mechanism, the convolutional block attention module, and the novel sinusoidal learnable attention. The attention mechanism is able to merge relevant information from activation maps at various levels of the network. The net takes as input raw audio files and it is able to fine tune also the features extraction phase. In fact, a Mel-like filter is designed during the training, thus adapting filter banks on important frequencies. AUCO ResNet has proved to provide state of art results on many datasets. Firstly, it has been tested on many datasets containing Covid-19 cough and breath. This choice is related to the fact that that cough and breath are language independent, allowing for cross dataset tests with generalization aims. These tests demonstrate that the approach can be adopted as a low cost, fast and remote Covid-19 pre-screening tool. The net has also been tested on the famous UrbanSound 8K dataset, achieving state of the art accuracy without any data preprocessing or data augmentation technique.	['Giuseppe Pirlo', 'Luigi Moretti', 'Donato Impedovo', 'Paolo Giglio', 'Vincenzo Dentamaro']	2022-03-15	null	null	null	pattern-recognition-2022-3	['environmental-sound-classification', 'sound-classification', 'covid-19-detection']	['audio', 'audio', 'medical']	[-1.20358326e-01 5.83958928e-04 4.18881208e-01 -6.63664797e-03 -3.71365875e-01 -2.34246567e-01 3.40081722e-01 3.01929832e-01 -8.83437812e-01 4.53342468e-01 2.12376580e-01 1.80922840e-02 -3.82811397e-01 -6.38527751e-01 -4.33702677e-01 -6.91147208e-01 -3.58728528e-01 4.26672429e-01 3.01335305e-01 -3.68391484e-01 9.65360180e-02 8.12407017e-01 -2.08414292e+00 2.16055512e-01 4.25626338e-01 9.93300498e-01 4.45510626e-01 1.03920090e+00 2.32339114e-01 6.89603567e-01 -7.38857746e-01 7.87265971e-02 -2.17835277e-01 -3.15842092e-01 -7.84614921e-01 -6.34006500e-01 2.84870476e-01 6.23061024e-02 -8.49403813e-02 7.22462714e-01 1.26171184e+00 1.05915964e-01 6.27896011e-01 -6.76292062e-01 -2.35494241e-01 9.08737481e-01 1.82628423e-01 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c5b19a57-bcb0-423e-8b85-39be92f363c2	variational-latent-discrete-representation	2306.15282	null	https://arxiv.org/abs/2306.15282v2	https://arxiv.org/pdf/2306.15282v2.pdf	Variational Latent Discrete Representation for Time Series Modelling	Discrete latent space models have recently achieved performance on par with their continuous counterparts in deep variational inference. While they still face various implementation challenges, these models offer the opportunity for a better interpretation of latent spaces, as well as a more direct representation of naturally discrete phenomena. Most recent approaches propose to train separately very high-dimensional prior models on the discrete latent data which is a challenging task on its own. In this paper, we introduce a latent data model where the discrete state is a Markov chain, which allows fast end-to-end training. The performance of our generative model is assessed on a building management dataset and on the publicly available Electricity Transformer Dataset.	['Sylvain Le Corff', 'Maurice Charbit', 'Max Cohen']	2023-06-27	null	null	null	null	['management']	['miscellaneous']	[ 5.22316769e-02 1.45488933e-01 -7.97931552e-02 -4.45867717e-01 -9.24246669e-01 -4.87461656e-01 1.30996776e+00 -1.84619635e-01 9.03508142e-02 6.04205489e-01 3.49223256e-01 -2.57767141e-01 -2.40577415e-01 -9.07905161e-01 -5.32000542e-01 -9.26056027e-01 1.89426571e-01 1.02527475e+00 -7.12683573e-02 1.30752161e-01 -6.20884970e-02 3.90404314e-01 -1.35969889e+00 4.62018438e-02 7.76762068e-01 6.43723428e-01 1.84631139e-01 5.76376021e-01 4.34570312e-02 6.67303979e-01 -2.97031373e-01 -6.33981302e-02 -8.38230401e-02 -3.19161564e-01 -5.40238917e-01 3.34595889e-01 1.53089613e-01 -5.70017457e-01 -3.99011850e-01 8.41472626e-01 1.05300933e-01 5.15103303e-02 1.10660458e+00 -1.40788269e+00 -6.13194823e-01 4.82728958e-01 -1.80300698e-01 3.51301990e-02 -4.22329381e-02 1.68640599e-01 1.33894122e+00 -6.45617425e-01 4.82838362e-01 1.36649835e+00 5.09347022e-01 2.23485023e-01 -2.00042725e+00 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4.47503865e-01 4.31972206e-01 -9.28121328e-01 -2.95321435e-01 2.66474336e-01]	[6.973645210266113, 3.8490617275238037]
5652de72-b0e3-4ed0-ba5e-42ead70e66c6	school-based-malaria-chemoprevention-as-a	2303.10684	null	https://arxiv.org/abs/2303.10684v1	https://arxiv.org/pdf/2303.10684v1.pdf	School-based malaria chemoprevention as a cost-effective approach to improve cognitive and educational outcomes: a meta-analysis	There is limited evidence of health interventions impact on cognitive function and educational outcomes. We build on two prior systematic reviews to conduct a meta-analysis, exploring the effects of one of the most consequential health interventions, malaria chemoprevention, on education outcomes. We pool data from nine study treatment groups (N=4,075) and outcomes across four countries. We find evidence of a positive effect (Cohen's d = 0.12, 95% CI [0.08, 0.16]) on student cognitive function, achieved at low cost. These results show that malaria chemoprevention can be highly cost effective in improving some cognitive skills, such as sustained attention. Moreover, we conduct simulations using a new common metric (learning-adjusted years of development) to compare cost-effectiveness across diverse interventions. While we might expect that traditional education interventions provide an immediate learning gain, health interventions such as malaria prevention can have surprisingly cost-effective education benefits, enabling children to achieve their full human capital potential.	['Lauren M. Cohee', 'Donald Bundy', 'Charles Opondo', 'R. Matthew Chico', 'Sian Clarke', 'Matthew C. H. Jukes', 'Noam Angrist']	2023-03-19	null	null	null	null	['metric-learning', 'metric-learning']	['computer-vision', 'methodology']	[ 4.33585010e-02 2.06327170e-01 -4.87583965e-01 -8.12349934e-03 -3.17467153e-01 -3.67669374e-01 4.86668587e-01 8.48744452e-01 -7.18067586e-01 6.57455206e-01 5.95902264e-01 -9.28987205e-01 -4.22601342e-01 -9.62546051e-01 -1.08584082e+00 -3.50594491e-01 -8.05291981e-02 -1.24170333e-01 1.11311655e-02 2.37745985e-01 6.74755931e-01 2.97258079e-01 -1.85209966e+00 -2.19907790e-01 1.52889276e+00 -2.73218453e-01 5.76571584e-01 5.17224073e-01 -2.38742471e-01 6.37510121e-01 -6.86902881e-01 -3.76526624e-01 -4.32013720e-01 -5.68732083e-01 -5.21312952e-01 -3.69325221e-01 3.60885441e-01 -7.90706813e-01 6.87558874e-02 6.48105681e-01 9.26281571e-01 -9.78632495e-02 6.82094693e-01 -8.76258612e-01 -9.45004523e-01 2.26022750e-01 -2.77877063e-01 2.53117234e-01 6.80417597e-01 1.96993485e-01 8.39008167e-02 -4.89788294e-01 3.21612954e-01 1.36970627e+00 6.11353338e-01 3.53723526e-01 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0c73d43d-849c-45d3-812a-e988029b3a86	the-benefits-of-close-domain-fine-tuning-for	1912.05846	null	https://arxiv.org/abs/1912.05846v1	https://arxiv.org/pdf/1912.05846v1.pdf	The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images	A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding. Nowadays, the most successful methods for table detection in document images employ deep learning algorithms; and, particularly, a technique known as fine-tuning. In this context, such a technique exports the knowledge acquired to detect objects in natural images to detect tables in document images. However, there is only a vague relation between natural and document images, and fine-tuning works better when there is a close relation between the source and target task. In this paper, we show that it is more beneficial to employ fine-tuning from a closer domain. To this aim, we train different object detection algorithms (namely, Mask R-CNN, RetinaNet, SSD and YOLO) using the TableBank dataset (a dataset of images of academic documents designed for table detection and recognition), and fine-tune them for several heterogeneous table detection datasets. Using this approach, we considerably improve the accuracy of the detection models fine-tuned from natural images (in mean a 17%, and, in the best case, up to a 60%).	['Jónathan Heras', 'César Domínguez', 'Ángela Casado-García', 'Vico Pascual', 'Eloy Mata']	2019-12-12	null	null	null	null	['table-detection']	['miscellaneous']	[ 1.43345788e-01 9.13986936e-02 2.04580091e-03 -2.34472919e-02 -8.58578324e-01 -9.92156088e-01 8.19556952e-01 5.47014713e-01 -4.49149340e-01 4.59925950e-01 1.20516434e-01 -7.16745527e-03 -3.62453945e-02 -9.48228955e-01 -9.57023025e-01 -4.94927913e-01 3.06512117e-01 8.86053741e-01 5.15295029e-01 -3.07007283e-01 3.43734294e-01 6.71518087e-01 -1.56002784e+00 8.54724944e-01 7.86991239e-01 1.12734568e+00 3.50338280e-01 6.81524515e-01 -3.80170882e-01 8.46234679e-01 -9.81500804e-01 -9.37771320e-01 8.14952925e-02 -5.06950438e-01 -8.41151655e-01 3.66703182e-01 4.27363843e-01 -2.29223207e-01 -1.37764186e-01 1.08659768e+00 2.01403290e-01 -1.82021961e-01 7.11963236e-01 -5.81690133e-01 -7.07214892e-01 1.11535490e+00 -3.94626200e-01 4.37537760e-01 4.42767799e-01 -1.32690355e-01 1.11893392e+00 -7.94408381e-01 7.71714330e-01 1.24865675e+00 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8efb0d84-1c74-4a5f-8fd4-871d3d56254f	sparsity-exploitation-via-discovering	2306.17090	null	https://arxiv.org/abs/2306.17090v1	https://arxiv.org/pdf/2306.17090v1.pdf	Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting	Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASSO	['Duy Khuong Nguyen', 'Truong Son Hy', 'Ngoc-Dung Do']	2023-06-29	null	null	null	null	['time-series-forecasting']	['time-series']	[-6.82821274e-02 5.73644154e-02 -2.32052878e-01 -5.20646989e-01 -1.28146455e-01 -4.96724218e-01 4.73493010e-01 4.53207530e-02 6.47902310e-01 4.50566858e-01 2.59766132e-01 -6.96081340e-01 -1.00549489e-01 -8.60294819e-01 -8.11671019e-01 -5.74120998e-01 -4.96008813e-01 4.14251745e-01 -1.28112167e-01 -3.53534907e-01 -7.28710368e-02 3.06616873e-01 -1.16471887e+00 1.59936666e-01 9.36290562e-01 1.00775230e+00 9.14832875e-02 4.54595953e-01 -1.69478402e-01 1.07680535e+00 -1.90545529e-01 -2.10332498e-01 2.34426245e-01 -5.65068781e-01 -3.09992999e-01 3.21444720e-02 1.60952523e-01 3.94128487e-02 -7.22883582e-01 9.48084176e-01 1.72957689e-01 2.13484675e-01 2.65612423e-01 -1.40568566e+00 -7.92410135e-01 1.05967510e+00 -5.23838460e-01 2.52384782e-01 1.17046237e-01 -6.84423223e-02 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9e549c8b-578d-4334-bddc-5112d27ab7df	self-organising-maps-in-computer-security	1608.01668	null	http://arxiv.org/abs/1608.01668v1	http://arxiv.org/pdf/1608.01668v1.pdf	Self-Organising Maps in Computer Security	Some argue that biologically inspired algorithms are the future of solving difficult problems in computer science. Others strongly believe that the future lies in the exploration of mathematical foundations of problems at hand. The field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities. The lack of rigorous scientific practices prevalent in biologically inspired security research does not aid in presenting bio-inspired security approaches as a viable way of dealing with complex security problems. This chapter introduces a biologically inspired algorithm, called the Self Organising Map (SOM), that was developed by Teuvo Kohonen in 1981. Since the algorithm's inception it has been scrutinised by the scientific community and analysed in more than 4000 research papers, many of which dealt with various computer security issues, from anomaly detection, analysis of executables all the way to wireless network monitoring. In this chapter a review of security related SOM research undertaken in the past is presented and analysed. The algorithm's biological analogies are detailed and the author's view on the future possibilities of this successful bio-inspired approach are given. The SOM algorithm's close relation to a number of vital functions of the human brain and the emergence of multi-core computer architectures are the two main reasons behind our assumption that the future of the SOM algorithm and its variations is promising, notably in the field of computer security.	['Jan Feyereisl', 'Uwe Aickelin']	2016-08-05	null	null	null	null	['computer-security']	['miscellaneous']	[ 3.07733029e-01 5.23561127e-02 2.23146543e-01 -7.96851050e-03 7.17903316e-01 -4.17018056e-01 8.76510382e-01 3.95356864e-01 -7.46474743e-01 6.17227614e-01 -5.49895577e-02 -5.06271243e-01 -5.44078112e-01 -8.67014647e-01 -2.61962600e-02 -9.73055005e-01 -1.77805752e-01 1.86120108e-01 5.12283623e-01 -7.94941247e-01 9.88623679e-01 8.59625220e-01 -1.82542861e+00 7.73801096e-03 2.28603140e-01 6.50029957e-01 -3.25922631e-02 6.64754331e-01 -1.86065316e-01 7.71671295e-01 -7.49062598e-01 -2.00144038e-01 -7.91748092e-02 -5.30499518e-01 -8.69855106e-01 -4.03278202e-01 -6.36673868e-01 2.54177779e-01 -4.66874009e-03 8.98566365e-01 5.24806917e-01 1.79484189e-01 6.12559021e-01 -1.21482503e+00 -2.12668121e-01 2.31952697e-01 -2.40016103e-01 6.60358906e-01 1.56910434e-01 -5.92930838e-02 3.98850143e-01 -6.26360059e-01 2.21329197e-01 9.60378528e-01 6.08914971e-01 7.69335926e-01 -9.50453103e-01 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f8f9fe18-7634-4e48-9f87-2a756adbe0ff	smm4h-2022-task-2-dataset-for-stance-and	null	null	https://aclanthology.org/2022.smm4h-1.53	https://aclanthology.org/2022.smm4h-1.53.pdf	SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19	This paper is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.	['Elena Tutubalina', 'Vera Davydova']	null	null	null	null	smm4h-coling-2022-10	['stance-detection', 'argument-mining']	['natural-language-processing', 'natural-language-processing']	[ 2.59080738e-01 9.07622993e-01 -7.60632336e-01 -4.28664625e-01 -7.45590448e-01 -3.79302979e-01 9.01287436e-01 1.30128455e+00 -6.61716521e-01 1.05356002e+00 9.75665331e-01 -7.58637488e-01 -1.28623322e-01 -6.21297538e-01 -7.83732235e-01 -1.91513389e-01 -7.42948875e-02 5.56599259e-01 1.09277256e-01 -5.10324538e-01 4.17796433e-01 -2.62987256e-01 -1.25861621e+00 7.62716830e-01 6.23221755e-01 5.04862607e-01 -4.02701735e-01 6.12265766e-01 1.42245352e-01 1.14292634e+00 -7.60722995e-01 -6.83992028e-01 -4.16652262e-01 -1.79716945e-01 -1.47364593e+00 -6.20934963e-01 1.06575591e-02 -1.19442714e-03 2.61471450e-01 7.30351210e-01 5.41702867e-01 -2.17053562e-01 3.77511084e-01 -1.27063453e+00 8.84750783e-02 1.27394986e+00 -4.26857680e-01 7.98483372e-01 8.51599038e-01 -2.91106015e-01 1.24252069e+00 -4.65893745e-01 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5d1fec2d-b44d-40ca-a804-589853675502	differentially-private-distributed-convex-1	2302.14514	null	https://arxiv.org/abs/2302.14514v1	https://arxiv.org/pdf/2302.14514v1.pdf	Differentially Private Distributed Convex Optimization	This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some constraints. In DO, each agent iteratively solves a local optimization model constructed by its own data and communicates some information (e.g., a local solution) with its neighbors until a global solution is obtained. Even though locally stored data are not shared with other agents, it is still possible to reconstruct the data from the information communicated among agents, which could limit the practical usage of DO in applications with sensitive data. To address this issue, we propose a privacy-preserving DO algorithm for constrained convex optimization models, which provides a statistical guarantee of data privacy, known as differential privacy, and a sequence of iterates that converges to an optimal solution in expectation. The proposed algorithm generalizes a linearized alternating direction method of multipliers by introducing a multiple local updates technique to reduce communication costs and incorporating an objective perturbation method in the local optimization models to compute and communicate randomized feasible local solutions that cannot be utilized to reconstruct the local data, thus preserving data privacy. Under the existence of convex constraints, we show that, while both algorithms provide the same level of data privacy, the objective perturbation used in the proposed algorithm can provide better solutions than does the widely adopted output perturbation method that randomizes the local solutions by adding some noise. We present the details of privacy and convergence analyses and numerically demonstrate the effectiveness of the proposed algorithm by applying it in two different applications, namely, distributed control of power flow and federated learning, where data privacy is of concern.	['Kibaek Kim', 'Minseok Ryu']	2023-02-28	null	null	null	null	['distributed-optimization']	['methodology']	[ 5.31916842e-02 3.29651237e-02 -3.03697646e-01 -1.85801640e-01 -8.44766557e-01 -8.72784555e-01 1.00801624e-02 5.25250077e-01 -6.54679358e-01 9.84877527e-01 1.16580948e-01 -8.48503634e-02 -4.07654852e-01 -9.02403831e-01 -5.99108934e-01 -1.48108566e+00 -2.18036950e-01 1.55953437e-01 -5.33564270e-01 1.31137341e-01 2.25953057e-01 5.38967609e-01 -9.77760792e-01 -1.92867965e-01 9.16602612e-01 1.03706348e+00 -5.91729395e-02 3.41327071e-01 2.46519879e-01 5.75044036e-01 -9.09359813e-01 -1.13270104e-01 9.29274619e-01 -3.85202378e-01 -6.08236074e-01 2.51774073e-01 7.90416077e-03 -4.24835831e-01 -4.51105013e-02 1.49397564e+00 4.84100431e-01 4.38054055e-01 2.68664584e-02 -1.53485370e+00 -4.46598917e-01 4.84345347e-01 -6.97992980e-01 -1.47379950e-01 2.11483851e-01 -2.80839782e-02 9.04389858e-01 -2.81910926e-01 6.21941388e-01 7.40850687e-01 5.47650158e-01 5.30493855e-01 -1.51528907e+00 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fb8cdd95-d4da-43da-abab-3dbf0ab4a4a5	multiple-fusion-adaptation-a-strong-framework	2112.00295	null	https://arxiv.org/abs/2112.00295v1	https://arxiv.org/pdf/2112.00295v1.pdf	Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation	This paper challenges the cross-domain semantic segmentation task, aiming to improve the segmentation accuracy on the unlabeled target domain without incurring additional annotation. Using the pseudo-label-based unsupervised domain adaptation (UDA) pipeline, we propose a novel and effective Multiple Fusion Adaptation (MFA) method. MFA basically considers three parallel information fusion strategies, i.e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion. Specifically, the online-offline pseudo label fusion encourages the adaptive training to pay additional attention to difficult regions that are easily ignored by offline pseudo labels, therefore retaining more informative details. While the other two fusion strategies may look standard, MFA pays significant efforts to raise the efficiency and effectiveness for integration, and succeeds in injecting all the three strategies into a unified framework. Experiments on two widely used benchmarks, i.e., GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes, show that our method significantly improves the semantic segmentation adaptation, and sets up new state of the art (58.2% and 62.5% mIoU, respectively). The code will be available at https://github.com/KaiiZhang/MFA.	['Xiaohui Hu', 'Haichang Li', 'Rui Wang', 'Yifan Sun', 'Kai Zhang']	2021-12-01	null	null	null	null	['unsupervised-semantic-segmentation', 'synthetic-to-real-translation']	['computer-vision', 'computer-vision']	[ 2.21366793e-01 2.11103827e-01 -1.87300384e-01 -5.28623581e-01 -1.19240332e+00 -5.72642922e-01 4.18466091e-01 6.50511086e-02 -6.10309601e-01 6.12295389e-01 -2.21393824e-01 -1.64279193e-01 2.19416142e-01 -6.16093993e-01 -5.86631119e-01 -7.24901974e-01 4.80811536e-01 6.53451681e-01 7.98327804e-01 -7.66100511e-02 -1.81556866e-01 1.30096488e-02 -1.20906866e+00 5.21233045e-02 1.33869636e+00 1.05062437e+00 3.60698730e-01 2.23251268e-01 -3.48737597e-01 4.53028738e-01 -3.97675127e-01 -5.69938540e-01 2.46674001e-01 -3.66267860e-01 -1.13438976e+00 2.54881412e-01 1.15388267e-01 -1.03406042e-01 1.34375185e-01 1.29843247e+00 4.04023588e-01 3.96295458e-01 5.30589461e-01 -1.10388505e+00 -4.04407322e-01 6.35124624e-01 -9.01568294e-01 -6.88665733e-03 -8.92803967e-02 -3.06409337e-02 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362c9902-c19f-4792-a15f-c66a90fc3324	a-mixed-reality-dataset-for-category-level-6d	2211.10470	null	https://arxiv.org/abs/2211.10470v1	https://arxiv.org/pdf/2211.10470v1.pdf	A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containers	Estimating the 6D pose and size of household containers is challenging due to large intra-class variations in the object properties, such as shape, size, appearance, and transparency. The task is made more difficult when these objects are held and manipulated by a person due to varying degrees of hand occlusions caused by the type of grasps and by the viewpoint of the camera observing the person holding the object. In this paper, we present a mixed-reality dataset of hand-occluded containers for category-level 6D object pose and size estimation. The dataset consists of 138,240 images of rendered hands and forearms holding 48 synthetic objects, split into 3 grasp categories over 30 real backgrounds. We re-train and test an existing model for 6D object pose estimation on our mixed-reality dataset. We discuss the impact of the use of this dataset in improving the task of 6D pose and size estimation.	['Andrea Cavallaro', 'Alessio Xompero', 'Xavier Weber']	2022-11-18	null	null	null	null	['mixed-reality', '6d-pose-estimation']	['computer-vision', 'computer-vision']	[-8.04860443e-02 -3.25654089e-01 2.28689656e-01 -3.00755471e-01 -2.34445557e-01 -9.56824303e-01 2.52231628e-01 4.63413484e-02 -1.51143089e-01 1.15458921e-01 2.43574500e-01 1.72574461e-01 2.04742700e-01 -3.16870779e-01 -1.02384830e+00 -4.39010292e-01 -2.72562951e-01 9.14963543e-01 2.45722264e-01 2.60229737e-01 2.57273078e-01 8.67617428e-01 -1.55302155e+00 2.92702049e-01 2.66849667e-01 1.05972111e+00 3.90700966e-01 9.56023991e-01 3.06890905e-01 4.20767903e-01 -7.93907106e-01 -2.39565954e-01 7.03224957e-01 2.28237107e-01 -3.18037182e-01 6.63764834e-01 8.89386952e-01 -9.85980511e-01 -3.06764454e-01 6.79139197e-01 5.12458205e-01 1.67926356e-01 8.54437351e-01 -1.24612856e+00 -3.16604733e-01 7.03916177e-02 -8.34277689e-01 -9.08740088e-02 9.19809639e-01 9.88600925e-02 4.33240473e-01 -7.26553082e-01 7.02271998e-01 1.59421778e+00 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08304d9a-65d0-4cd3-a85a-7e0c3151d36b	temporal-event-knowledge-acquisition-via	1805.10956	null	http://arxiv.org/abs/1805.10956v1	http://arxiv.org/pdf/1805.10956v1.pdf	Temporal Event Knowledge Acquisition via Identifying Narratives	Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large text corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperform several recent neural network models on the narrative cloze task.	['Wenlin Yao', 'Ruihong Huang']	2018-05-28	temporal-event-knowledge-acquisition-via-1	https://aclanthology.org/P18-1050	https://aclanthology.org/P18-1050.pdf	acl-2018-7	['temporal-relation-classification']	['natural-language-processing']	[ 6.90268949e-02 -1.06692992e-01 -9.11599100e-01 -3.06336403e-01 -6.13362670e-01 -9.26717818e-01 1.50077188e+00 5.46313524e-01 -4.03417826e-01 9.83562708e-01 1.37918925e+00 -1.73823014e-01 -4.56101716e-01 -8.66401434e-01 -6.97716951e-01 -1.73858345e-01 -3.77652138e-01 2.42078304e-01 2.91965842e-01 -3.68689358e-01 3.48476976e-01 1.60631344e-01 -9.64215875e-01 1.16420496e+00 2.12975621e-01 5.70090711e-01 3.45281400e-02 5.36501348e-01 -2.54462153e-01 2.06671000e+00 -6.50653422e-01 -6.30647779e-01 -4.51327533e-01 -8.23077738e-01 -1.33258295e+00 -3.30017000e-01 -1.35975122e-01 -1.82266533e-01 -1.13613200e+00 2.62245476e-01 1.98954511e-02 5.18177330e-01 9.79798555e-01 -8.99897754e-01 -7.04264641e-01 1.68774676e+00 -2.64009953e-01 1.02124918e+00 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1.78017631e-01 1.48271427e-01 1.35433865e+00 -4.99393493e-01 1.04581034e+00 5.32097399e-01 -3.23532343e-01 4.43483829e-01 -9.82892096e-01 -2.50092685e-01 5.06578445e-01 2.52328962e-01 -4.86686110e-01 -1.43138140e-01 7.85773277e-01]	[10.851222038269043, 8.91960334777832]
45470468-6a28-4427-a791-076c112a4996	self-supervised-shadow-removal	2010.11619	null	https://arxiv.org/abs/2010.11619v1	https://arxiv.org/pdf/2010.11619v1.pdf	Self-Supervised Shadow Removal	Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents. Decades of re-search produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shad-owed and shadow-free training image pairs. In this work,we propose an unsupervised single image shadow removal solution via self-supervised learning by using a conditioned mask. In contrast to existing literature, we do not require paired shadowed and shadow-free images, instead we rely on self-supervision and jointly learn deep models to remove and add shadows to images. We validate our approach on the recently introduced ISTD and USR datasets. We largely improve quantitatively and qualitatively over the compared methods and set a new state-of-the-art performance in single image shadow removal.	['Radu Timofte', 'Luc van Gool', 'Andres Romero', 'Florin-Alexandru Vasluianu']	2020-10-22	null	null	null	null	['shadow-removal', 'image-shadow-removal']	['computer-vision', 'computer-vision']	[ 9.67347383e-01 7.28518888e-02 3.99736434e-01 -3.51149350e-01 -4.38829720e-01 -2.81728894e-01 6.34768665e-01 -4.68777418e-01 -4.45642442e-01 1.00425637e+00 1.72609147e-02 -2.64144927e-01 1.49372727e-01 -2.02458784e-01 -7.47134209e-01 -9.05562758e-01 3.73359293e-01 3.96279663e-01 5.74286878e-01 -1.19696788e-01 2.36141041e-01 6.55652046e-01 -1.82774830e+00 2.65188795e-02 1.10634673e+00 7.33725965e-01 8.10634673e-01 6.51517510e-01 2.49694183e-01 7.11230874e-01 -5.61111510e-01 -1.17877237e-01 4.69985634e-01 -4.26644176e-01 -4.23866779e-01 6.21130884e-01 9.13400948e-01 -6.34765685e-01 -5.84429145e-01 7.14595258e-01 3.73208284e-01 1.05287395e-01 5.45810759e-01 -1.07986403e+00 -6.11247003e-01 -2.78260976e-01 -7.42660463e-01 1.46396875e-01 3.62296939e-01 1.69108003e-01 5.07061839e-01 -9.70536649e-01 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5.16829193e-01 -1.61593151e+00 8.44127405e-03 -6.74689889e-01 -8.01460326e-01 3.94736230e-01 8.86857867e-01 -8.62304151e-01 -6.77907407e-01 2.95270920e-01]	[10.830410957336426, -4.089817047119141]
71704da5-b1ec-46ce-b1a9-943cd31f98fe	allophant-cross-lingual-phoneme-recognition	2306.04306	null	https://arxiv.org/abs/2306.04306v1	https://arxiv.org/pdf/2306.04306v1.pdf	Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes	This paper proposes Allophant, a multilingual phoneme recognizer. It requires only a phoneme inventory for cross-lingual transfer to a target language, allowing for low-resource recognition. The architecture combines a compositional phone embedding approach with individually supervised phonetic attribute classifiers in a multi-task architecture. We also introduce Allophoible, an extension of the PHOIBLE database. When combined with a distance based mapping approach for grapheme-to-phoneme outputs, it allows us to train on PHOIBLE inventories directly. By training and evaluating on 34 languages, we found that the addition of multi-task learning improves the model's capability of being applied to unseen phonemes and phoneme inventories. On supervised languages we achieve phoneme error rate improvements of 11 percentage points (pp.) compared to a baseline without multi-task learning. Evaluation of zero-shot transfer on 84 languages yielded a decrease in PER of 2.63 pp. over the baseline.	['Munir Georges', 'Aaricia Herygers', 'Kevin Glocker']	2023-06-07	null	null	null	null	['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer']	['natural-language-processing', 'natural-language-processing']	[ 1.94135606e-01 5.12148738e-02 -2.57034123e-01 -4.51602310e-01 -1.76404905e+00 -9.34505343e-01 6.05694532e-01 -1.14997253e-01 -6.95639372e-01 6.48768306e-01 3.16207498e-01 -4.78220463e-01 5.10899484e-01 -4.64630097e-01 -1.01094544e+00 -4.13999945e-01 3.16093475e-01 1.09462428e+00 2.25234535e-02 -7.48737082e-02 -1.53481495e-02 2.96076030e-01 -1.25936902e+00 7.23325908e-01 8.28535676e-01 7.67010152e-01 4.17853326e-01 7.13765740e-01 -2.47872785e-01 2.53568232e-01 -4.61195946e-01 -5.24863601e-01 1.52568668e-01 -1.50867209e-01 -7.65592635e-01 -3.79146963e-01 1.17148030e+00 -5.68405539e-02 7.25785345e-02 5.64596891e-01 5.69736063e-01 -3.55897844e-02 9.68389869e-01 -7.99814403e-01 -8.26363921e-01 6.41261458e-01 -8.85195751e-03 1.18622221e-01 3.00848573e-01 -1.66317344e-01 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3.40175956e-01 -5.93260646e-01 -4.65932280e-01 -1.08029056e+00 -4.90032405e-01 1.84521545e-02 2.88181663e-01 -2.41021842e-01 -3.32709014e-01 1.15561383e-02]	[14.254584312438965, 7.007551193237305]
5dc798b7-067b-46c8-b429-4a6906848d77	adaptive-human-matting-for-dynamic-videos	2304.06018	null	https://arxiv.org/abs/2304.06018v1	https://arxiv.org/pdf/2304.06018v1.pdf	Adaptive Human Matting for Dynamic Videos	The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We address this limitation by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a framework designed for simultaneously differentiating foregrounds from backgrounds and capturing alpha matte details of human subjects in the foreground. Two interconnected network designs are employed to achieve this goal: (1) an encoder-decoder network that produces alpha mattes and intermediate masks which are used to guide the transformer in adaptively decoding foregrounds and backgrounds, and (2) a transformer network in which long- and short-term attention combine to retain spatial and temporal contexts, facilitating the decoding of foreground details. We benchmark and study our methods on recently introduced datasets, showing that our model notably improves matting realism and temporal coherence in complex real-world videos and achieves new best-in-class generalizability. Further details and examples are available at https://github.com/microsoft/AdaM.	['Zicheng Liu', 'Lijuan Wang', 'Linjie Li', 'Kevin Lin', 'Kun Luo', 'Jiang Wang', 'Chung-Ching Lin']	2023-04-12	null	http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.pdf	cvpr-2023-1	['image-matting', 'video-matting']	['computer-vision', 'computer-vision']	[ 3.89994979e-01 -1.61723256e-01 -4.61571291e-03 -2.80503631e-01 -5.10918081e-01 -4.14761633e-01 4.71263021e-01 -5.48399746e-01 -8.01744163e-02 4.74290997e-01 4.51702476e-01 -1.43106878e-01 2.67045170e-01 -5.09584427e-01 -9.41020250e-01 -6.86248362e-01 -8.42675865e-02 2.72830725e-01 6.40119255e-01 5.44209033e-02 -2.34702155e-02 2.17205182e-01 -1.47396564e+00 8.88776183e-01 8.38128746e-01 9.26096797e-01 3.25465053e-01 9.18842494e-01 -7.93675706e-02 1.09425056e+00 -4.51249272e-01 -6.58504665e-01 5.37097991e-01 -5.75234890e-01 -5.44980586e-01 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9cc224c2-4bf2-471f-9a7a-d3f65966215e	event-transition-planning-for-open-ended-text	2204.09453	null	https://arxiv.org/abs/2204.09453v1	https://arxiv.org/pdf/2204.09453v1.pdf	Event Transition Planning for Open-ended Text Generation	Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context. The open-ended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays. Despite these neural models are good at producing human-like text, it is difficult for them to arrange causalities and relations between given facts and possible ensuing events. To bridge this gap, we propose a novel two-stage method which explicitly arranges the ensuing events in open-ended text generation. Our approach can be understood as a specially-trained coarse-to-fine algorithm, where an event transition planner provides a "coarse" plot skeleton and a text generator in the second stage refines the skeleton. Experiments on two open-ended text generation tasks demonstrate that our proposed method effectively improves the quality of the generated text, especially in coherence and diversity. The code is available at: \url{https://github.com/qtli/EventPlanforTextGen}.	['Lingpeng Kong', 'Yuxuan Lai', 'Zhaochun Ren', 'Wei Bi', 'Piji Li', 'Qintong Li']	2022-04-20	null	https://aclanthology.org/2022.findings-acl.269	https://aclanthology.org/2022.findings-acl.269.pdf	findings-acl-2022-5	['story-completion']	['natural-language-processing']	[ 2.77900934e-01 6.68938577e-01 -6.86142519e-02 -2.26368636e-01 -6.79512262e-01 -5.61511278e-01 1.11794174e+00 1.04158446e-01 4.99719866e-02 1.10699475e+00 8.99723530e-01 -2.31078893e-01 2.86052767e-02 -9.49033618e-01 -6.33868277e-01 -3.22648138e-01 2.50260562e-01 6.37852073e-01 -1.52345508e-01 -5.17444670e-01 4.21098232e-01 -8.47848579e-02 -1.32545066e+00 4.65022832e-01 1.15471745e+00 3.35075140e-01 4.65952426e-01 8.55952144e-01 -3.24626356e-01 9.13881123e-01 -7.33467340e-01 -4.95350033e-01 -1.81810215e-01 -1.14245033e+00 -9.65851605e-01 8.30650479e-02 -3.17449212e-01 -4.18350875e-01 -3.92504424e-01 6.30293250e-01 5.18522441e-01 4.23695028e-01 9.85731959e-01 -1.15450561e+00 -7.41539598e-01 1.30486262e+00 -4.27759707e-01 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5cc4c94f-6a11-4817-8de1-0e35ca8cae8c	sensenet-neural-keyphrase-generation-with	2012.06754	null	https://arxiv.org/abs/2012.06754v1	https://arxiv.org/pdf/2012.06754v1.pdf	SenSeNet: Neural Keyphrase Generation with Document Structure	Keyphrase Generation (KG) is the task of generating central topics from a given document or literary work, which captures the crucial information necessary to understand the content. Documents such as scientific literature contain rich meta-sentence information, which represents the logical-semantic structure of the documents. However, previous approaches ignore the constraints of document logical structure, and hence they mistakenly generate keyphrases from unimportant sentences. To address this problem, we propose a new method called Sentence Selective Network (SenSeNet) to incorporate the meta-sentence inductive bias into KG. In SenSeNet, we use a straight-through estimator for end-to-end training and incorporate weak supervision in the training of the sentence selection module. Experimental results show that SenSeNet can consistently improve the performance of major KG models based on seq2seq framework, which demonstrate the effectiveness of capturing structural information and distinguishing the significance of sentences in KG task.	['Xuanjing Huang', 'Qi Zhang', 'Xiaoyu Xing', 'Bingning Wang', 'Zhengyan Li', 'Yichao Luo']	2020-12-12	null	null	null	null	['keyphrase-generation']	['natural-language-processing']	[ 4.67514366e-01 4.28602785e-01 -2.59050906e-01 -3.85525703e-01 -1.03618622e+00 -4.89544094e-01 6.71092093e-01 2.12276220e-01 -4.53340262e-01 1.08276558e+00 1.05989277e+00 -2.00015351e-01 2.30981022e-01 -8.83011758e-01 -8.89814794e-01 -4.15513784e-01 4.45888042e-01 7.80381933e-02 3.67176645e-02 -4.09882575e-01 7.01698780e-01 -2.29028940e-01 -1.27990210e+00 6.52743578e-01 1.15069842e+00 5.38981557e-01 6.02629364e-01 7.93038249e-01 -5.79181850e-01 1.03874147e+00 -8.80053341e-01 -4.41995502e-01 -1.94988146e-01 -8.25796306e-01 -9.62796628e-01 -2.10639611e-01 5.17618120e-01 -1.92708716e-01 -1.58155501e-01 1.23175049e+00 7.85918951e-01 9.83273312e-02 3.78971130e-01 -9.18780506e-01 -6.12761557e-01 1.46530449e+00 -2.73459405e-01 3.03819776e-01 2.80278563e-01 -1.06424756e-01 1.50474072e+00 -9.55117106e-01 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cfcd2be5-93e7-4ae5-a809-cce413ff2044	decodingtrust-a-comprehensive-assessment-of	2306.11698	null	https://arxiv.org/abs/2306.11698v1	https://arxiv.org/pdf/2306.11698v1.pdf	DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models	Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications to healthcare and finance - where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives - including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially due to the reason that GPT-4 follows the (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/.	['Bo Li', 'Dawn Song', 'Sanmi Koyejo', 'Yu Cheng', 'Zinan Lin', 'Dan Hendrycks', 'Mantas Mazeika', 'Simran Arora', 'Sang T. Truong', 'Rylan Schaeffer', 'Ritik Dutta', 'Zidi Xiong', 'Chejian Xu', 'Chenhui Zhang', 'Mintong Kang', 'Chulin Xie', 'Hengzhi Pei', 'Weixin Chen', 'Boxin Wang']	2023-06-20	null	null	null	null	['adversarial-robustness', 'ethics']	['adversarial', 'miscellaneous']	[-1.23950854e-01 5.02372205e-01 -6.44508079e-02 -3.66201282e-01 -9.95347738e-01 -1.08279455e+00 7.17866242e-01 4.67331707e-02 1.07914330e-02 8.18495810e-01 2.02263042e-01 -8.12479258e-01 2.20960066e-01 -5.05748570e-01 -9.42700028e-01 -5.02580881e-01 -7.59161934e-02 2.67564297e-01 -3.53861302e-01 -3.01277667e-01 1.11356759e-02 8.54518116e-02 -4.37702000e-01 2.35718757e-01 6.42249167e-01 7.99122691e-01 -9.11293805e-01 5.07228076e-01 7.82664061e-01 1.05193675e+00 -7.23015189e-01 -1.61731696e+00 3.46703202e-01 -2.40181178e-01 -7.89712369e-01 -6.46830976e-01 1.90188289e-01 -6.78373754e-01 -3.31941932e-01 1.14500165e+00 8.29465151e-01 -5.44862866e-01 3.33139896e-01 -1.88172150e+00 -1.00852358e+00 1.21456814e+00 -1.10205896e-01 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dfdb67be-35c0-4eaf-b4f2-510092700a2e	sequential-point-clouds-a-survey	2204.09337	null	https://arxiv.org/abs/2204.09337v2	https://arxiv.org/pdf/2204.09337v2.pdf	Sequential Point Clouds: A Survey	Point cloud has drawn more and more research attention as well as real-world applications. However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud data could provide is still limited. Recently, researchers put more and more effort into sequential point clouds. This paper presents an extensive review of the deep learning-based methods for sequential point cloud research including dynamic flow estimation, object detection \& tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Finally, this paper is concluded by discussing the challenges in the current sequential point cloud research and pointing out insightful potential future research directions.	['YingLi Tian', 'HaiYan Wang']	2022-04-20	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[-4.42254692e-01 -6.96575701e-01 -3.20567787e-01 -2.39044651e-01 7.02748299e-02 -6.62344635e-01 5.35449147e-01 1.84443429e-01 -2.54179418e-01 2.89613515e-01 -6.91958070e-01 -5.95531523e-01 8.02410617e-02 -8.74411941e-01 -7.22008526e-01 -5.92888653e-01 -2.90486634e-01 7.66785204e-01 5.32553136e-01 -2.03036204e-01 7.05724776e-01 1.25984168e+00 -1.90703726e+00 -3.92196812e-02 8.41884375e-01 1.06564236e+00 4.70118850e-01 7.04629958e-01 -5.93344510e-01 1.82051077e-01 -5.32930791e-01 -2.21065551e-01 5.94126880e-01 3.41051310e-01 -4.36677188e-01 5.83518222e-02 7.67190456e-01 -5.08504450e-01 -3.63103390e-01 1.04674387e+00 2.21001655e-01 3.39258403e-01 3.36299449e-01 -1.79667294e+00 -4.31339949e-01 -1.53542310e-01 -8.34635735e-01 5.48319578e-01 -3.87292318e-02 5.14270961e-01 5.23789883e-01 -1.13525712e+00 5.73429585e-01 1.32665205e+00 6.89834654e-01 2.97755390e-01 -5.19321859e-01 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de360762-7ed4-46e5-a3ac-46c25f936ef2	protagonists-tagger-in-literary-domain-new	2110.01349	null	https://arxiv.org/abs/2110.01349v1	https://arxiv.org/pdf/2110.01349v1.pdf	Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage	Semantic annotation of long texts, such as novels, remains an open challenge in Natural Language Processing (NLP). This research investigates the problem of detecting person entities and assigning them unique identities, i.e., recognizing people (especially main characters) in novels. We prepared a method for person entity linkage (named entity recognition and disambiguation) and new testing datasets. The datasets comprise 1,300 sentences from 13 classic novels of different genres that a novel reader had manually annotated. Our process of identifying literary characters in a text, implemented in protagonistTagger, comprises two stages: (1) named entity recognition (NER) of persons, (2) named entity disambiguation (NED) - matching each recognized person with the literary character's full name, based on approximate text matching. The protagonistTagger achieves both precision and recall of above 83% on the prepared testing sets. Finally, we gathered a corpus of 13 full-text novels tagged with protagonistTagger that comprises more than 35,000 mentions of literary characters.	['Anna Wróblewska', 'Weronika Łajewska']	2021-10-04	null	null	null	null	['entity-disambiguation']	['natural-language-processing']	[-4.57401387e-02 1.63279489e-01 1.40642017e-01 -3.88870060e-01 -8.02287519e-01 -8.42690349e-01 1.01631129e+00 5.19949019e-01 -1.04955661e+00 1.28973055e+00 5.66835880e-01 2.85834104e-01 3.77387479e-02 -1.06652844e+00 -5.84019125e-01 -2.87157893e-01 3.32367718e-01 1.20921075e+00 3.17235380e-01 -1.80030331e-01 4.07174438e-01 5.07303178e-01 -1.17424655e+00 1.20503247e-01 8.33245695e-01 4.18286771e-01 -6.65788651e-02 6.36622667e-01 -5.41953027e-01 4.47049260e-01 -1.02476490e+00 -1.26288760e+00 5.56326434e-02 -1.81735158e-01 -1.26203907e+00 -3.54228497e-01 2.59763032e-01 3.16911221e-01 -2.88876384e-01 9.66941535e-01 7.43874848e-01 5.14197350e-01 7.52136171e-01 -1.14550698e+00 -7.24240959e-01 1.28396213e+00 -4.11370575e-01 4.10976887e-01 5.95935881e-01 -5.41817605e-01 1.02968287e+00 -8.56462836e-01 1.29589331e+00 1.19786489e+00 7.61319518e-01 6.31167471e-01 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3d46e332-1bb1-4a73-aa61-42cb50e05f32	deep-dual-stream-residual-network-with	2207.12004	null	https://arxiv.org/abs/2207.12004v1	https://arxiv.org/pdf/2207.12004v1.pdf	Deep dual stream residual network with contextual attention for pansharpening of remote sensing images	Pansharpening enhances spatial details of high spectral resolution multispectral images using features of high spatial resolution panchromatic image. There are a number of traditional pansharpening approaches but producing an image exhibiting high spectral and spatial fidelity is still an open problem. Recently, deep learning has been used to produce promising pansharpened images; however, most of these approaches apply similar treatment to both multispectral and panchromatic images by using the same network for feature extraction. In this work, we present present a novel dual attention-based two-stream network. It starts with feature extraction using two separate networks for both images, an encoder with attention mechanism to recalibrate the extracted features. This is followed by fusion of the features forming a compact representation fed into an image reconstruction network to produce a pansharpened image. The experimental results on the Pl\'{e}iades dataset using standard quantitative evaluation metrics and visual inspection demonstrates that the proposed approach performs better than other approaches in terms of pansharpened image quality.	['Muhammad Shahzad', 'Anis Ur Rahman', 'Syeda Roshana Ali']	2022-07-25	null	null	null	null	['pansharpening']	['computer-vision']	[ 8.65052760e-01 -5.80323398e-01 -5.15767485e-02 -2.67060786e-01 -9.13985848e-01 -3.49225134e-01 4.82096016e-01 -2.59031981e-01 -4.68331009e-01 6.67102695e-01 9.99401063e-02 -3.34665366e-02 -6.22971237e-01 -1.15256691e+00 -4.69648749e-01 -1.07174599e+00 4.88675497e-02 -1.63357690e-01 2.27568537e-01 -5.47652245e-01 1.84052750e-01 8.30011725e-01 -1.58688974e+00 3.12970370e-01 9.65850234e-01 1.06581485e+00 5.56075454e-01 7.55478978e-01 4.94939953e-01 6.80481315e-01 -3.12981039e-01 -4.84679528e-02 7.81371713e-01 -2.92708993e-01 -7.69032001e-01 4.42421973e-01 5.23373008e-01 -5.11672080e-01 -3.45565259e-01 1.44823337e+00 4.81569052e-01 2.31935516e-01 3.47423196e-01 -8.04185867e-01 -1.12868738e+00 5.64924002e-01 -1.29492164e+00 4.37618345e-01 -2.13872641e-03 -1.24516793e-01 9.51815546e-01 -6.07906699e-01 1.64228991e-01 9.21906769e-01 6.57319844e-01 -1.80317208e-01 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-1.08540070e+00 3.20199907e-01 6.38503551e-01 -4.17595297e-01 -1.22862384e-01 7.17210919e-02]	[10.138443946838379, -1.9282076358795166]
129247ac-a2bf-4fd9-a6d8-53403fb8fec6	radiff-controllable-diffusion-models-for	2307.02392	null	https://arxiv.org/abs/2307.02392v1	https://arxiv.org/pdf/2307.02392v1.pdf	RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation	Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from the vast amount of data it will allow acquiring. Automated source finding is a particularly important task in this context, as it enables the detection and classification of astronomical objects. Deep-learning-based object detection and semantic segmentation models have proven to be suitable for this purpose. However, training such deep networks requires a high volume of labeled data, which is not trivial to obtain in the context of radio astronomy. Since data needs to be manually labeled by experts, this process is not scalable to large dataset sizes, limiting the possibilities of leveraging deep networks to address several tasks. In this work, we propose RADiff, a generative approach based on conditional diffusion models trained over an annotated radio dataset to generate synthetic images, containing radio sources of different morphologies, to augment existing datasets and reduce the problems caused by class imbalances. We also show that it is possible to generate fully-synthetic image-annotation pairs to automatically augment any annotated dataset. We evaluate the effectiveness of this approach by training a semantic segmentation model on a real dataset augmented in two ways: 1) using synthetic images obtained from real masks, and 2) generating images from synthetic semantic masks. We show an improvement in performance when applying augmentation, gaining up to 18% in performance when using real masks and 4% when augmenting with synthetic masks. Finally, we employ this model to generate large-scale radio maps with the objective of simulating Data Challenges.	['Concetto Spampinato', 'Filomena Bufano', 'Cristobal Bordiu', 'Adriano Ingallinera', 'Eva Sciacca', 'Simone Riggi', 'Daniel Magro', 'Andrew M. Hopkins', 'Andrea Pilzer', 'Giuseppe Fiameni', 'Andrea DeMarco', 'Thomas Cecconello', 'Renato Sortino']	2023-07-05	null	null	null	null	['object-detection', 'astronomy']	['computer-vision', 'miscellaneous']	[ 5.35714447e-01 2.91250497e-01 3.20869952e-01 -2.72709668e-01 -9.96538579e-01 -6.71354175e-01 7.19855309e-01 -3.81163247e-02 -5.74372172e-01 7.78393269e-01 -1.76084042e-01 -2.50910312e-01 -1.32698938e-01 -9.42158580e-01 -8.92560720e-01 -5.45010746e-01 1.35636941e-01 9.90546107e-01 3.31150830e-01 -4.33596484e-02 1.83880746e-01 7.85302877e-01 -1.56454229e+00 5.76250860e-03 7.44464099e-01 1.03855217e+00 6.59475505e-01 6.06080651e-01 -1.92658752e-01 6.61148608e-01 -7.74580359e-01 -3.38610560e-02 5.71239471e-01 -6.42463684e-01 -6.69697881e-01 2.88037747e-01 3.90235372e-02 3.56329395e-03 3.50127034e-02 7.81188667e-01 3.37587386e-01 7.94178322e-02 6.93843186e-01 -1.08212566e+00 4.16749232e-02 2.88135231e-01 -4.72286373e-01 2.00993299e-01 2.61282381e-02 1.44996256e-01 5.79659641e-01 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c3f4ad1c-7b71-45f0-91a5-c3213cda1d8b	graph-construction-using-principal-axis-trees	2302.12000	null	https://arxiv.org/abs/2302.12000v2	https://arxiv.org/pdf/2302.12000v2.pdf	Graph Construction using Principal Axis Trees for Simple Graph Convolution	Graph Neural Networks (GNNs) are increasingly becoming the favorite method for graph learning. They exploit the semi-supervised nature of deep learning, and they bypass computational bottlenecks associated with traditional graph learning methods. In addition to the feature matrix $X$, GNNs need an adjacency matrix $A$ to perform feature propagation. In many cases the adjacency matrix $A$ is missing. We introduce a graph construction scheme that construct the adjacency matrix $A$ using unsupervised and supervised information. Unsupervised information characterize the neighborhood around points. We used Principal Axis trees (PA-trees) as a source of unsupervised information, where we create edges between points falling onto the same leaf node. For supervised information, we used the concept of penalty and intrinsic graphs. A penalty graph connects points with different class labels, whereas intrinsic graph connects points with the same class label. We used the penalty and intrinsic graphs to remove or add edges to the graph constructed via PA-tree. This graph construction scheme was tested on two well-known GNNs: 1) Graph Convolutional Network (GCN) and 2) Simple Graph Convolution (SGC). The experiments show that it is better to use SGC because it is faster and delivers better or the same results as GCN. We also test the effect of oversmoothing on both GCN and SGC. We found out that the level of smoothing has to be selected carefully for SGC to avoid oversmoothing.	['Masahiro Takatsuka', 'Adel F. Ahmed', 'John Stavrakakis', 'Mashaan Alshammari']	2023-02-22	null	null	null	null	['graph-partitioning']	['graphs']	[-1.39366016e-01 4.16855484e-01 2.32237905e-01 -4.70189691e-01 1.59425437e-01 -4.33780611e-01 4.32673305e-01 4.88242298e-01 -4.22274083e-01 4.19459671e-01 -1.40231445e-01 -4.35664177e-01 -2.63267338e-01 -1.35979354e+00 -8.17703724e-01 -7.79687405e-01 -7.46565402e-01 2.41843998e-01 3.59809577e-01 -7.08331838e-02 -1.21822130e-04 7.40171790e-01 -1.33740449e+00 3.59391682e-02 7.70243466e-01 7.87455261e-01 7.83917978e-02 4.95116144e-01 -5.54182112e-01 6.50516272e-01 -3.50798905e-01 -2.90071458e-01 5.42573929e-01 -4.59563166e-01 -7.28450119e-01 1.67431936e-01 3.81578296e-01 3.04459751e-01 -4.61258411e-01 1.38619089e+00 1.88630298e-01 2.46090680e-01 6.53202057e-01 -1.33673203e+00 -4.85898167e-01 8.85988533e-01 -6.70039594e-01 9.99984983e-03 -5.81631437e-02 6.52425550e-03 1.07786834e+00 -5.34114063e-01 7.26660311e-01 1.16728795e+00 9.70339417e-01 2.63763279e-01 -1.32260227e+00 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5e514ee3-8640-4d2b-91e0-e2c1800ed91f	a-neural-network-trained-to-predict-future	1805.10734	null	http://arxiv.org/abs/1805.10734v2	http://arxiv.org/pdf/1805.10734v2.pdf	A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception	While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial. However, while CNNs capture key properties of the average responses of cortical neurons, they fail to explain other properties of these neurons. For one, CNNs typically require large quantities of labeled input data for training. Our own brains, in contrast, rarely have access to this kind of supervision, so to the extent that representations are similar between CNNs and brains, this similarity must arise via different training paths. In addition, neurons in visual cortex produce complex time-varying responses even to static inputs, and they dynamically tune themselves to temporal regularities in the visual environment. We argue that these differences are clues to fundamental differences between the computations performed in the brain and in deep networks. To begin to close the gap, here we study the emergent properties of a previously-described recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single unit response dynamics to complex perceptual motion illusions. These results suggest potentially deep connections between recurrent predictive neural network models and the brain, providing new leads that can enrich both fields.	['William Lotter', 'David Cox', 'Gabriel Kreiman']	2018-05-28	null	null	null	null	['predict-future-video-frames']	['computer-vision']	[ 4.13207054e-01 9.92854238e-02 1.85201824e-01 -2.69775480e-01 1.63768977e-01 -6.63235903e-01 8.26894760e-01 -1.96271315e-01 -1.36723965e-01 5.65226734e-01 2.13688284e-01 -1.83619305e-01 -8.61213543e-03 -7.95155346e-01 -8.20733428e-01 -9.85709548e-01 -5.98061793e-02 1.29482418e-01 2.86108315e-01 -4.04337406e-01 1.47740781e-01 5.47568917e-01 -1.50783384e+00 5.20331860e-01 2.62032688e-01 9.52488303e-01 3.98342192e-01 9.14073348e-01 2.71418422e-01 1.17935371e+00 -3.07711661e-01 1.19893350e-01 8.24741423e-02 -6.83626652e-01 -8.03781629e-01 7.20083565e-02 1.04367830e-01 1.96763545e-01 -8.13220382e-01 8.36250424e-01 1.55976683e-01 -4.19588163e-02 8.72543454e-01 -8.58581245e-01 -9.16548729e-01 1.19267628e-01 -2.76321117e-02 5.99743247e-01 -1.38237745e-01 6.34319007e-01 9.29726422e-01 -5.50792336e-01 7.10217357e-01 1.09560955e+00 7.02319622e-01 7.65882015e-01 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db890700-9224-4292-8abf-ac0c4b6b78ca	identity-deception-detection	null	null	https://aclanthology.org/I17-1089	https://aclanthology.org/I17-1089.pdf	Identity Deception Detection	This paper addresses the task of detecting identity deception in language. Using a novel identity deception dataset, consisting of real and portrayed identities from 600 individuals, we show that we can build accurate identity detectors targeting both age and gender, with accuracies of up to 88. We also perform an analysis of the linguistic patterns used in identity deception, which lead to interesting insights into identity portrayers.	['Quincy Davenport', "Ver{\\'o}nica P{\\'e}rez-Rosas", 'Anna Mengdan Dai', 'Mohamed Abouelenien', 'Rada Mihalcea']	2017-11-01	identity-deception-detection-1	https://aclanthology.org/I17-1089	https://aclanthology.org/I17-1089.pdf	ijcnlp-2017-11	['deception-detection']	['miscellaneous']	[ 2.92227864e-02 3.44462171e-02 -8.25948343e-02 -7.48921692e-01 -4.61741596e-01 -9.55305219e-01 1.30508399e+00 5.42083308e-02 -2.17404827e-01 7.71093607e-01 4.43172961e-01 -8.43572170e-02 5.99470794e-01 -6.57303393e-01 -4.49180514e-01 -2.24898309e-01 -2.04694510e-01 4.13263053e-01 -4.48235035e-01 -4.80323732e-01 6.39309704e-01 3.80719870e-01 -1.60559309e+00 5.13390124e-01 8.03509951e-01 1.00311756e+00 -1.09125865e+00 6.65560305e-01 -4.39916290e-02 1.05180657e+00 -1.11580193e+00 -1.38588595e+00 1.27881244e-01 -5.17767608e-01 -8.32319856e-01 -2.43155360e-02 1.38296664e+00 -3.06848556e-01 -4.73849922e-01 1.19840086e+00 3.84782195e-01 -3.16364735e-01 8.07748973e-01 -1.32496703e+00 -1.38027692e+00 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5f2326f8-8ff2-4ac1-8e6b-22488cf33150	balanced-energy-regularization-loss-for-out-1	2306.10485	null	https://arxiv.org/abs/2306.10485v1	https://arxiv.org/pdf/2306.10485v1.pdf	Balanced Energy Regularization Loss for Out-of-distribution Detection	In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalance in the distribution of the auxiliary OOD data across classes. We propose a balanced energy regularization loss that is simple but generally effective for a variety of tasks. Our balanced energy regularization loss utilizes class-wise different prior probabilities for auxiliary data to address the class imbalance in OOD data. The main concept is to regularize auxiliary samples from majority classes, more heavily than those from minority classes. Our approach performs better for OOD detection in semantic segmentation, long-tailed image classification, and image classification than the prior energy regularization loss. Furthermore, our approach achieves state-of-the-art performance in two tasks: OOD detection in semantic segmentation and long-tailed image classification. Code is available at https://github.com/hyunjunChhoi/Balanced_Energy.	['Jin Young Choi', 'Hawook Jeong', 'Hyunjun Choi']	2023-06-18	balanced-energy-regularization-loss-for-out	http://openaccess.thecvf.com//content/CVPR2023/html/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.pdf	cvpr-2023-1	['out-of-distribution-detection']	['computer-vision']	[ 3.34143117e-02 1.65460929e-01 -4.10574526e-01 -4.30113941e-01 -1.18346786e+00 -2.53895283e-01 2.58214563e-01 2.04291835e-01 -2.20344499e-01 3.81303221e-01 -1.50106931e-02 9.00306851e-02 3.62857789e-01 -4.73917633e-01 -6.27792120e-01 -9.44499433e-01 4.98472989e-01 5.32831550e-01 3.42149228e-01 2.16728792e-01 3.03263843e-01 2.44816303e-01 -1.75094819e+00 -9.73929390e-02 1.16352141e+00 1.28112841e+00 2.87178516e-01 3.08886856e-01 -1.02718785e-01 3.00567031e-01 -5.75889528e-01 -7.19742104e-02 3.98756415e-01 -4.34636235e-01 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77dc500f-0b3e-4946-9af8-a385ab7f8452	serialized-interacting-mixed-membership	2209.07813	null	https://arxiv.org/abs/2209.07813v1	https://arxiv.org/pdf/2209.07813v1.pdf	Serialized Interacting Mixed Membership Stochastic Block Model	Last years have seen a regain of interest for the use of stochastic block modeling (SBM) in recommender systems. These models are seen as a flexible alternative to tensor decomposition techniques that are able to handle labeled data. Recent works proposed to tackle discrete recommendation problems via SBMs by considering larger contexts as input data and by adding second order interactions between contexts' related elements. In this work, we show that these models are all special cases of a single global framework: the Serialized Interacting Mixed membership Stochastic Block Model (SIMSBM). It allows to model an arbitrarily large context as well as an arbitrarily high order of interactions. We demonstrate that SIMSBM generalizes several recent SBM-based baselines. Besides, we demonstrate that our formulation allows for an increased predictive power on six real-world datasets.	['Sabine Loudcher', 'Julien Velcin', 'Gaël Poux-Médard']	2022-09-16	null	null	null	null	['stochastic-block-model']	['graphs']	[-8.97913277e-02 -2.26897776e-01 -4.58979070e-01 -3.97181183e-01 -4.20054585e-01 -6.50295198e-01 1.08149981e+00 3.42767267e-03 -6.73575625e-02 4.17971164e-01 7.09205270e-01 -7.01275766e-01 -5.30228257e-01 -5.76699436e-01 -7.44738221e-01 -8.13557267e-01 -3.67718518e-01 7.07600474e-01 2.00214818e-01 -6.23152018e-01 2.27644462e-02 3.06271911e-01 -1.52427042e+00 7.81470299e-01 6.48714006e-01 4.84959126e-01 1.74778536e-01 3.53340089e-01 -2.29505464e-01 7.61184394e-01 -1.78844020e-01 -6.07787967e-01 3.23270380e-01 3.08989324e-02 -8.88994157e-01 -8.58733896e-03 5.22213101e-01 -3.74434255e-02 -2.45259717e-01 5.25916159e-01 1.14242826e-02 5.11943400e-01 5.72317064e-01 -1.15862310e+00 -5.30134261e-01 1.07993960e+00 -4.56381083e-01 1.86415032e-01 5.04459143e-01 -4.14887667e-01 1.63473272e+00 -9.76787746e-01 4.89616901e-01 1.42897916e+00 5.15243292e-01 4.57815915e-01 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6.34113967e-01 3.17245334e-01 -8.92908335e-01 -2.83287257e-01 2.91961548e-03]	[9.611677169799805, 5.499703884124756]
9c75a92f-d936-417a-a849-d1472992fb00	clip-art-contrastive-pre-training-for-fine-1	2204.14244	null	https://arxiv.org/abs/2204.14244v1	https://arxiv.org/pdf/2204.14244v1.pdf	CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification	Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. To the best of our knowledge, we are one of the first methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural network on a variety of artwork images and text descriptions pairs. CLIP is able to learn directly from free-form art descriptions, or, if available, curated fine-grained labels. Model's zero-shot capability allows predicting accurate natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset, which we consider the largest annotated artwork dataset. In this benchmark we achieved competitive results using only self-supervision.	['Kerem Turgutlu', 'Marcos V. Conde']	2022-04-29	clip-art-contrastive-pre-training-for-fine	https://openaccess.thecvf.com/content/CVPR2021W/CVFAD/html/Conde_CLIP-Art_Contrastive_Pre-Training_for_Fine-Grained_Art_Classification_CVPRW_2021_paper.html	https://openaccess.thecvf.com/content/CVPR2021W/CVFAD/papers/Conde_CLIP-Art_Contrastive_Pre-Training_for_Fine-Grained_Art_Classification_CVPRW_2021_paper.pdf	proceedings-of-the-ieee-cvf-conference-on	['fine-grained-visual-recognition']	['computer-vision']	[ 3.87619525e-01 -2.46269792e-01 -3.27925950e-01 -4.81320024e-01 -1.00210977e+00 -8.39837670e-01 1.01192760e+00 -1.08232170e-01 -2.59476125e-01 5.54232419e-01 9.62060317e-02 3.05261761e-01 -3.50919813e-01 -5.91061592e-01 -9.12240446e-01 -2.71915197e-01 4.43727702e-01 1.35477388e+00 -1.12856358e-01 7.95277059e-02 3.27604026e-01 4.50383216e-01 -1.86563826e+00 6.46243453e-01 4.24714714e-01 1.46318769e+00 3.28374058e-01 7.30536342e-01 -6.10184133e-01 1.08839428e+00 -3.13243777e-01 -7.39521086e-01 3.59601438e-01 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965c0834-459a-4fe5-91ee-82df1489a944	learning-deformable-kernels-for-image-and	1904.06903	null	http://arxiv.org/abs/1904.06903v1	http://arxiv.org/pdf/1904.06903v1.pdf	Learning Deformable Kernels for Image and Video Denoising	Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input. Instead of relying on hand-crafted selecting and averaging strategies, we propose to explicitly learn this process with deep neural networks. Specifically, we propose deformable 2D kernels for image denoising where the sampling locations and kernel weights are both learned. The proposed kernel naturally adapts to image structures and could effectively reduce the oversmoothing artifacts. Furthermore, we develop 3D deformable kernels for video denoising to more efficiently sample pixels across the spatial-temporal space. Our method is able to solve the misalignment issues of large motion from dynamic scenes. For better training our video denoising model, we introduce the trilinear sampler and a new regularization term. We demonstrate that the proposed method performs favorably against the state-of-the-art image and video denoising approaches on both synthetic and real-world data.	['Muchen Li', 'Xiangyu Xu', 'Wenxiu Sun']	2019-04-15	null	null	null	null	['video-denoising']	['computer-vision']	[ 2.77687043e-01 -5.40511072e-01 3.98082197e-01 -3.93631697e-01 -9.68565047e-01 -4.43115324e-01 5.54381490e-01 -3.22209656e-01 -7.02123761e-01 5.50362527e-01 1.34146705e-01 1.80605203e-01 -1.18001238e-01 -6.65762663e-01 -8.40974927e-01 -1.12581909e+00 -3.03655453e-02 -2.06839547e-01 2.20186129e-01 -8.42895657e-02 2.27292746e-01 3.81233603e-01 -1.33284581e+00 1.54773071e-01 1.10994232e+00 8.98286521e-01 2.49018863e-01 6.16988242e-01 1.80236727e-01 5.10902643e-01 -3.05935502e-01 -2.78675616e-01 4.77240831e-01 -4.27577436e-01 -2.10021809e-01 4.63965356e-01 8.12092900e-01 -4.75710601e-01 -5.52683294e-01 1.32140744e+00 4.24008369e-01 4.00498033e-01 4.95865077e-01 -6.01515710e-01 -6.38961256e-01 7.59907067e-02 -8.04270804e-01 2.24863991e-01 8.19120836e-03 3.27409297e-01 5.41820288e-01 -9.19567764e-01 4.63174433e-01 1.30352497e+00 9.57117617e-01 4.79833275e-01 -1.56793010e+00 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6.75709724e-01 6.39846802e-01 -5.84995627e-01 -6.52933419e-01 5.02560973e-01]	[11.473234176635742, -2.231491804122925]
d116d8f1-58a2-4dca-803e-d1e4674da97c	automatic-grammatical-error-correction-for	null	null	https://aclanthology.org/P19-1609	https://aclanthology.org/P19-1609.pdf	Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study	Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation. We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence compression and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring task-oriented improvements in the tasks where target sentences are in a formal style.	['Furu Wei', 'Tao Ge', 'Ming Zhou', 'Xingxing Zhang']	2019-07-01	null	null	null	acl-2019-7	['formality-style-transfer', 'sentence-compression']	['natural-language-processing', 'natural-language-processing']	[ 7.06516743e-01 6.09223843e-01 1.96233034e-01 -6.47700310e-01 -1.04049933e+00 -4.83903706e-01 5.24081767e-01 1.08474284e-01 -2.49391466e-01 1.52047932e+00 6.00589991e-01 -5.69233775e-01 3.88158619e-01 -8.64300370e-01 -8.84948730e-01 4.72453516e-03 4.05078590e-01 7.57036030e-01 -2.45532319e-01 -8.78850758e-01 6.12038195e-01 -1.53177932e-01 -1.17642915e+00 7.13693440e-01 1.52610528e+00 1.11336887e-01 4.11501676e-01 1.20323968e+00 -4.53767359e-01 7.13581324e-01 -1.22621500e+00 -7.11662531e-01 -2.53265146e-02 -1.00018573e+00 -1.08190346e+00 -5.28666079e-01 6.80331588e-01 -1.50613636e-01 2.18103975e-02 9.89579618e-01 8.70288253e-01 5.22779673e-03 6.16359234e-01 -8.35363448e-01 -9.20573473e-01 1.19522023e+00 8.08729529e-02 2.83942640e-01 6.53888583e-01 2.44383037e-01 9.24881339e-01 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ee4c7e56-8260-4dbd-8195-721f56e0b222	radiology-text-analysis-system-radtext	2204.09599	null	https://arxiv.org/abs/2204.09599v1	https://arxiv.org/pdf/2204.09599v1.pdf	Radiology Text Analysis System (RadText): Architecture and Evaluation	Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis. In this work, we present RadText, an open-source radiology text analysis system developed by Python. RadText offers an easy-to-use text analysis pipeline, including de-identification, section segmentation, sentence split and word tokenization, named entity recognition, parsing, and negation detection. RadText features a flexible modular design, provides a hybrid text processing schema, and supports raw text processing and local processing, which enables better usability and improved data privacy. RadText adopts BioC as the unified interface, and also standardizes the input / output into a structured representation compatible with Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This allows for a more systematic approach to observational research across multiple, disparate data sources. We evaluated RadText on the MIMIC-CXR dataset, with five new disease labels we annotated for this work. RadText demonstrates highly accurate classification performances, with an average precision of, a recall of 0.94, and an F-1 score of 0.92. We have made our code, documentation, examples, and the test set available at https://github.com/bionlplab/radtext .	['Yifan Peng', 'Zhiyong Lu', 'George Shih', 'Ying Ding', 'Mingquan Lin', 'Song Wang']	2022-03-19	null	null	null	null	['negation-detection']	['natural-language-processing']	[ 1.00133084e-01 1.45527527e-01 -4.41931993e-01 -5.04147649e-01 -1.39962530e+00 -5.95336854e-01 4.08122353e-02 1.21520174e+00 -6.77428544e-01 4.81207639e-01 7.31468678e-01 -8.87442112e-01 -3.63551974e-01 -5.04381359e-01 -2.15367779e-01 -4.97874826e-01 1.84756026e-01 4.84464347e-01 -2.28931289e-02 3.94698858e-01 7.27587044e-02 4.38008696e-01 -8.65921021e-01 7.47887015e-01 7.72593915e-01 5.84965765e-01 2.15988249e-01 8.88308346e-01 -1.56691179e-01 1.09656465e+00 -3.02429855e-01 -4.59571004e-01 -9.16286930e-02 -9.18685496e-02 -1.16874719e+00 -3.79308224e-01 8.78012925e-02 -3.01033854e-01 -3.16602796e-01 5.91840923e-01 8.11376095e-01 -3.08646590e-01 4.44219172e-01 -3.48906279e-01 -4.32399064e-01 8.34293425e-01 -3.22191805e-01 3.19797546e-01 4.71805364e-01 2.08829880e-01 7.64140368e-01 -4.86183375e-01 1.01186860e+00 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1.02558649e+00 2.32892796e-01 3.34941059e-01 -3.92203420e-01 -8.09789419e-01 -3.62877458e-01 1.79703802e-01 1.03970788e-01 -6.99413717e-02 5.59276082e-02 1.14706181e-01]	[8.402182579040527, 8.654760360717773]
39d90ed2-b02f-4606-9224-3349d661e626	the-steep-road-to-happily-ever-after-an	1904.03366	null	http://arxiv.org/abs/1904.03366v1	http://arxiv.org/pdf/1904.03366v1.pdf	The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models	Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally, we make recommendations for addressing these limitations in the future.	['Natalie Parde', 'Yatri Modi']	2019-04-06	the-steep-road-to-happily-ever-after-an-1	https://aclanthology.org/W19-1805	https://aclanthology.org/W19-1805.pdf	ws-2019-6	['visual-storytelling']	['natural-language-processing']	[ 1.22381926e-01 2.36078352e-01 -2.99349949e-02 -8.37228298e-02 -2.75893092e-01 -7.97333777e-01 6.66265965e-01 2.89859921e-01 9.01777819e-02 5.76101065e-01 7.15985835e-01 -6.75838768e-01 -1.19779864e-02 -3.34048092e-01 -5.31620741e-01 5.83915645e-03 -1.63334593e-01 -1.78804733e-02 3.85704309e-01 -2.79897511e-01 8.82807493e-01 2.70241112e-01 -1.76379097e+00 5.01045406e-01 5.88707149e-01 3.52315068e-01 1.06379718e-01 8.19616258e-01 -3.76073003e-01 1.30741131e+00 -1.13421369e+00 -3.45621318e-01 -1.90382332e-01 -6.23791814e-01 -9.26544368e-01 3.39236557e-01 4.78208512e-01 -3.79500270e-01 -3.48591238e-01 7.04231262e-01 5.13875782e-01 1.78312749e-01 3.04328978e-01 -1.59813845e+00 -7.91095197e-01 4.15221661e-01 -3.63723189e-01 5.39641798e-01 1.14832592e+00 3.56996357e-01 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eb1414b0-ee0e-4bdc-ac7e-e968f7a0a966	perceptual-generative-adversarial-networks	1706.05274	null	http://arxiv.org/abs/1706.05274v2	http://arxiv.org/pdf/1706.05274v2.pdf	Perceptual Generative Adversarial Networks for Small Object Detection	Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.	['Xiaodan Liang', 'Yunchao Wei', 'Jiashi Feng', 'Tingfa Xu', 'Shuicheng Yan', 'Jianan Li']	2017-06-16	perceptual-generative-adversarial-networks-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.pdf	cvpr-2017-7	['small-object-detection']	['computer-vision']	[ 3.47217888e-01 1.65639430e-01 9.75429565e-02 -1.57910854e-01 -1.00206435e+00 -4.43428934e-01 4.60597098e-01 -7.68960565e-02 -2.39345372e-01 5.44185340e-01 -2.29554743e-01 1.67169925e-02 4.69944894e-01 -9.93321836e-01 -9.02572989e-01 -9.03883576e-01 -6.40242696e-02 5.36618173e-01 7.96895981e-01 -2.44903713e-01 -8.38534459e-02 3.66368949e-01 -1.47894990e+00 6.80719018e-01 8.57841611e-01 1.08960283e+00 4.07469273e-01 8.15059483e-01 3.91300797e-01 8.63202393e-01 -1.11554825e+00 -3.67682308e-01 5.82218528e-01 -5.24476051e-01 -1.80571988e-01 -4.82321382e-02 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2fee74cf-6ead-4e40-9ee3-f506ee427d74	ihs_rd-lexical-normalization-for-english	null	null	https://aclanthology.org/W15-4311	https://aclanthology.org/W15-4311.pdf	IHS\_RD: Lexical Normalization for English Tweets	null	['Viachaslau Patsepnia', 'Dmitry Supranovich']	2015-07-01	null	null	null	ws-2015-7	['lexical-normalization']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 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-1.23056793e+00 -8.58632207e-01 -1.05834293e+00 -1.40667629e+00 -1.19437599e+00 -5.12782812e-01 1.89310342e-01 3.83317530e-01 3.90289724e-01 -2.06836104e-01 2.85260603e-02 9.80917513e-01 -1.07949340e+00 -3.90341938e-01 -9.39418614e-01 -1.00026262e+00 -7.84516707e-02 2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01]	[-7.3938212394714355, 3.7356112003326416]
074b9416-ea4d-4fe4-b5d7-d4e09c255487	on-the-use-of-different-feature-extraction	1406.7314	null	http://arxiv.org/abs/1406.7314v1	http://arxiv.org/pdf/1406.7314v1.pdf	On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels	The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different features extraction methods such as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) with several features normalization techniques like rasta filtering and cepstral mean subtraction (CMS). Based on this, a comparative evaluation of these features is performed on the task of text independent speaker identification using a combination between gaussian mixture models (GMM) and linear and non-linear kernels based on support vector machine (SVM).	['Imen Trabelsi', 'Dorra Ben Ayed']	2014-06-27	null	null	null	null	['robust-speech-recognition']	['speech']	[ 1.06248178e-01 -4.47244406e-01 4.44777645e-02 -3.14487547e-01 -5.83157778e-01 -4.66718018e-01 9.62333500e-01 2.77549654e-01 -4.26404208e-01 6.21277750e-01 4.38402206e-01 -3.94649982e-01 -1.94763750e-01 -1.93699613e-01 1.62019148e-01 -9.44181561e-01 -2.00062633e-01 -2.03543261e-01 2.67179042e-01 -1.37864873e-01 6.25094652e-01 8.70253444e-01 -1.89718270e+00 3.14649791e-01 5.88069558e-01 1.01105261e+00 1.53717309e-01 1.21092761e+00 -1.23512156e-01 7.20940292e-01 -8.58920217e-01 -1.54516071e-01 -1.12347327e-01 -1.58274069e-01 -5.56099832e-01 -6.27986714e-02 -1.45274594e-01 6.40168637e-02 -1.80843815e-01 1.05902803e+00 6.79488540e-01 7.42533743e-01 1.10303497e+00 -1.12630630e+00 -5.68154395e-01 4.64360386e-01 -2.91924700e-02 7.07424104e-01 4.99219090e-01 -3.70246530e-01 2.60587960e-01 -1.02961111e+00 -1.39330626e-01 1.37908542e+00 6.54110193e-01 2.24927887e-01 -1.00755417e+00 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