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4267d6df-f76d-4a5d-8b92-18fa1751d458	ia-gcn-interpretable-attention-based-graph	2103.15587	null	https://arxiv.org/abs/2103.15587v1	https://arxiv.org/pdf/2103.15587v1.pdf	IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction	Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination. Moreover, most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the model in a post hoc fashion. In this paper, we propose an interpretable graph learning-based model which 1) interprets the clinical relevance of the input features towards the task, 2) uses the explanation to improve the model performance and, 3) learns a population level latent graph that may be used to interpret the cohort's behavior. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. The main novelty lies in the interpretable attention module (IAM), which directly operates on multi-modal features. Our IAM learns the attention for each feature based on the unique interpretability-specific losses. We show the application on two publicly available datasets, Tadpole and UKBB, for three tasks of disease, age, and gender prediction. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task. Further, we show exhaustive validation and clinical interpretation of our results.	['Nassir Navab', 'Soroush Farghadani', 'Anees Kazi']	2021-03-29	null	null	null	null	['gender-prediction']	['computer-vision']	[ 4.36604768e-01 1.04113901e+00 -2.69206822e-01 -5.49207568e-01 -4.05048043e-01 -1.60105452e-01 4.35531795e-01 7.41058111e-01 -2.05927402e-01 4.75007176e-01 3.79511505e-01 -5.71516991e-01 -5.44749737e-01 -5.28978050e-01 -5.02817333e-01 -7.17952073e-01 -2.45108694e-01 9.95998085e-01 -3.53548676e-01 -1.68715324e-02 -2.51595676e-01 2.61974752e-01 -1.06661427e+00 4.18165565e-01 1.05650270e+00 9.99781132e-01 -2.54492164e-01 7.16576695e-01 2.56979942e-01 8.91942143e-01 -2.72567838e-01 -7.98644483e-01 -8.63357484e-02 -5.27534723e-01 -1.07113194e+00 4.72867638e-02 1.96794525e-01 -1.68797150e-01 -2.20173255e-01 7.12221861e-01 3.51763397e-01 -3.56727064e-01 1.03470647e+00 -1.41698694e+00 -7.56346822e-01 8.73935342e-01 -3.66525620e-01 2.25272626e-01 -1.42608047e-01 2.61575371e-01 1.22978938e+00 -4.15136099e-01 4.31247056e-01 1.31661642e+00 7.13549733e-01 8.60646129e-01 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e059c5cc-eb69-4cad-9d35-1a7a041c0ab0	select-good-regions-for-deblurring-based-on	2008.05065	null	https://arxiv.org/abs/2008.05065v1	https://arxiv.org/pdf/2008.05065v1.pdf	Select Good Regions for Deblurring based on Convolutional Neural Networks	The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel. Most of image deblurring approaches focus on developing image priors, however, there is not enough attention to the influence of image details and structures on the blur kernel estimation. What is the useful image structure and how to choose a good deblurring region? In this work, we propose a deep neural network model method for selecting good regions to estimate blur kernel. First we construct image patches with labels and train a deep neural networks, then the learned model is applied to determine which region of the image is most suitable to deblur. Experimental results illustrate that the proposed approach is effective, and could be able to select good regions for image deblurring.	['Xiaotian Wu', 'Xinglong Sun', 'Hang Yang']	2020-08-12	null	null	null	null	['blind-image-deblurring']	['computer-vision']	[ 1.55542925e-01 -4.85942125e-01 -1.94021482e-02 -1.64628446e-01 -3.17787409e-01 -5.05873144e-01 2.19585836e-01 -6.95677102e-01 -1.04520231e-01 7.44338691e-01 5.70868015e-01 -6.55649826e-02 -1.73194036e-01 -3.63356352e-01 -6.64399743e-01 -9.28223968e-01 2.69413173e-01 -1.95603848e-01 7.46015012e-02 1.53288543e-01 5.50545156e-01 4.86954778e-01 -1.13178694e+00 9.35684219e-02 1.13266683e+00 7.58499146e-01 7.89334774e-01 6.57277405e-01 2.28905037e-01 1.18372786e+00 -6.91742122e-01 2.68681854e-01 1.63232386e-01 -7.37268746e-01 -1.05449593e+00 4.64403927e-01 4.03809994e-01 -8.46644461e-01 -6.05964839e-01 1.53101766e+00 5.05466402e-01 2.33564377e-01 6.54609680e-01 -4.80414212e-01 -1.37490499e+00 6.05430067e-01 -6.41852677e-01 6.66807175e-01 -7.39416981e-04 1.09751828e-01 2.58080006e-01 -8.67704153e-01 2.95705497e-01 9.34490085e-01 4.35483575e-01 5.34502506e-01 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7204fb7a-71ac-41b9-bffb-293a00a916f6	pas-mef-multi-exposure-image-fusion-based-on	2105.11809	null	https://arxiv.org/abs/2105.11809v1	https://arxiv.org/pdf/2105.11809v1.pdf	PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map	High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers. With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. To minimize the information loss and produce high quality HDR-like images for LDR screens, this study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method relying on principal component analysis, adaptive well-exposedness and saliency maps. These weight maps are later refined through a guided filter and the fusion is carried out by employing a pyramidal decomposition. Experimental comparisons with existing techniques demonstrate that the proposed method produces very strong statistical and visual results.	['Mehmet Turkan', 'Oguzhan Ulucan', 'Diclehan Karakaya']	2021-05-25	null	null	null	null	['multi-exposure-image-fusion']	['computer-vision']	[ 6.95042312e-01 -4.28056329e-01 3.38991702e-01 -3.30857784e-02 -1.28335923e-01 -2.18641862e-01 4.63614136e-01 -8.39212239e-02 -3.04373682e-01 6.56728029e-01 2.76970595e-01 -6.78898990e-02 -1.80057794e-01 -7.24123776e-01 -3.18512142e-01 -6.36166990e-01 2.48718366e-01 -4.18161452e-01 7.48346448e-01 -3.65827292e-01 4.57882285e-01 6.27482951e-01 -2.06219769e+00 2.11400926e-01 1.03973627e+00 7.81638622e-01 8.27025354e-01 7.02024937e-01 9.68686044e-02 5.95495641e-01 -3.60243946e-01 -2.48009339e-01 4.02400613e-01 -4.11605567e-01 -2.92459905e-01 3.51832330e-01 2.14909166e-01 -4.68781024e-01 -3.27624619e-01 1.24583268e+00 5.65988302e-01 4.10254925e-01 5.34174323e-01 -5.59049666e-01 -1.02769136e+00 -1.34381101e-01 -1.20334530e+00 4.53498751e-01 8.39504719e-01 4.64326739e-02 3.40686142e-01 -1.01703417e+00 6.41510010e-01 1.15400612e+00 3.05968165e-01 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438e1797-a105-4710-adae-36feae5784a4	an-empirical-study-of-remote-sensing	2204.02825	null	https://arxiv.org/abs/2204.02825v4	https://arxiv.org/pdf/2204.02825v4.pdf	An Empirical Study of Remote Sensing Pretraining	Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights. Since natural images inevitably present a large domain gap relative to aerial images, probably limiting the finetuning performance on downstream aerial scene tasks. This issue motivates us to conduct an empirical study of remote sensing pretraining (RSP) on aerial images. To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks. Then, we investigate the impact of RSP on representative downstream tasks including scene recognition, semantic segmentation, object detection, and change detection using these CNN and vision transformer backbones. Empirical study shows that RSP can help deliver distinctive performances in scene recognition tasks and in perceiving RS related semantics such as "Bridge" and "Airplane". We also find that, although RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, it may still suffer from task discrepancies, where downstream tasks require different representations from scene recognition tasks. These findings call for further research efforts on both large-scale pretraining datasets and effective pretraining methods. The codes and pretrained models will be released at https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing.	['DaCheng Tao', 'Gui-Song Xia', 'Bo Du', 'Jing Zhang', 'Di Wang']	2022-04-06	null	null	null	null	['object-detection-in-aerial-images', 'scene-recognition', 'change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing']	['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous']	[ 6.15162790e-01 -2.87536472e-01 8.83178264e-02 -5.79688907e-01 -2.45788619e-01 -7.95904517e-01 4.09639239e-01 -3.28497112e-01 -4.93726939e-01 2.56257862e-01 4.15878110e-02 -5.94050825e-01 -1.52656391e-01 -1.09572697e+00 -9.12959814e-01 -4.85560358e-01 1.03101037e-01 -1.66609824e-01 1.86002031e-01 -6.04106307e-01 -5.97306602e-02 6.60827696e-01 -1.67868865e+00 2.78757393e-01 7.62865543e-01 9.84460354e-01 5.41766763e-01 6.58163369e-01 4.78156283e-02 6.59137726e-01 -4.38366294e-01 -7.97779709e-02 7.40318894e-01 -1.38204843e-01 -9.65265930e-01 2.21908435e-01 7.76802599e-01 -5.79889536e-01 -3.70593995e-01 1.18721950e+00 4.93975490e-01 3.14854056e-01 2.58284837e-01 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458ef21e-342c-405f-ad1b-af31e890c9e0	self-trained-one-class-classification-for	2106.06115	null	https://arxiv.org/abs/2106.06115v2	https://arxiv.org/pdf/2106.06115v2.pdf	Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection	Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effective for cases with fully or partially labeled data, that setting is in practice less common due to labeling being particularly tedious for this task. In this paper, we focus on fully unsupervised AD, in which the entire training dataset, containing both normal and anomalous samples, is unlabeled. To tackle this problem effectively, we propose to improve the robustness of one-class classification trained on self-supervised representations using a data refinement process. Our proposed data refinement approach is based on an ensemble of one-class classifiers (OCCs), each of which is trained on a disjoint subset of training data. Representations learned by self-supervised learning on the refined data are iteratively updated as the data refinement improves. We demonstrate our method on various unsupervised AD tasks with image and tabular data. With a 10% anomaly ratio on CIFAR-10 image data / 2.5% anomaly ratio on Thyroid tabular data, the proposed method outperforms the state-of-the-art one-class classifier by 6.3 AUC and 12.5 average precision / 22.9 F1-score.	['Tomas Pfister', 'Chen-Yu Lee', 'Sercan O. Arik', 'Chun-Liang Li', 'Kihyuk Sohn', 'Jinsung Yoon']	2021-06-11	self-supervise-refine-repeat-improving	https://openreview.net/forum?id=Nct9j3BVswZ	https://openreview.net/pdf?id=Nct9j3BVswZ	null	['one-class-classifier', 'one-class-classification']	['methodology', 'miscellaneous']	[ 3.94524992e-01 2.48075604e-01 -1.16800837e-01 -6.41612470e-01 -8.46727848e-01 -2.98064172e-01 4.14014786e-01 5.80295205e-01 -3.86169374e-01 6.26552343e-01 -2.05009386e-01 -6.20653629e-02 -8.08834285e-02 -6.38150871e-01 -5.42815030e-01 -8.14181864e-01 -8.71512070e-02 5.92117786e-01 3.04364502e-01 -4.05535474e-03 1.77095860e-01 3.86577934e-01 -1.71233952e+00 3.02389681e-01 1.09034753e+00 1.38299906e+00 -5.70378482e-01 5.49198389e-01 -1.73212498e-01 7.52352238e-01 -6.29848361e-01 -1.31974295e-01 4.33948070e-01 -3.09760541e-01 -7.25217819e-01 4.64141548e-01 6.07608438e-01 -9.43809450e-02 6.13945536e-04 1.14821291e+00 1.78029627e-01 1.05040736e-01 8.23043346e-01 -1.56687081e+00 -4.45075303e-01 3.08994472e-01 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8bccdc91-f6e8-4f71-b45e-6d16e2438440	rethinking-deep-face-restoration	null	null	https://openreview.net/forum?id=-AY7C3f26C_	https://openreview.net/pdf?id=-AY7C3f26C_	Rethinking Deep Face Restoration	A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications. While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces. Because the human visual system is very sensitive to faces, even minor facial changes may alter the identity and significantly degrade the perceptual quality. In this work, we argue the problems of existing models can be traced down to the two sub-tasks of the face restoration problem, i.e. face generation and face reconstruction, and the fragile balance between them. Based on the observation, we propose a new face restoration model that improves both generation and reconstruction by learning a stochastic model and enhancing the latent features respectively. Furthermore, we adapt the number of skip connections for a better balance between the two sub-tasks. Besides the model improvement, we also introduce a new evaluation metric for measuring models' ability to preserve the identity in the restored faces. Extensive experiments demonstrate that our model achieves state-of-the-art performance on multiple face restoration benchmarks. The user study shows that our model produces higher quality faces while better preserving the identity $86.4\%$ of the time compared with the best performing baselines.	['Xuhui Jia', 'Changyou Chen', 'Yukun Zhu', 'Marius Renn', 'Yandong Li', 'Chun-Te Chu', 'Yu-Chuan Su', 'Yang Zhao']	2021-09-29	null	http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.pdf	cvpr-2022-1	['face-reconstruction']	['computer-vision']	[ 3.04278582e-01 -7.75750801e-02 -4.34407871e-03 -6.00652874e-01 -7.11694300e-01 -2.61842728e-01 4.80703533e-01 -5.33540845e-01 1.32834271e-01 5.84745884e-01 5.40075660e-01 1.34130478e-01 1.65227324e-01 -8.48554015e-01 -8.43816698e-01 -8.12198520e-01 2.41623923e-01 -1.26115099e-01 -2.65794456e-01 -3.58784825e-01 1.46504313e-01 6.41626537e-01 -1.99567497e+00 5.82374275e-01 9.08700585e-01 9.91774321e-01 -1.64035887e-01 2.99535871e-01 -1.43068917e-02 6.56235576e-01 -5.09669960e-01 -8.15156221e-01 4.88371551e-01 -5.26038527e-01 -7.60088146e-01 2.73998529e-01 9.39693391e-01 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44cb9471-37a4-4fbb-bbdb-5931b29c593f	use-of-variational-inference-in-music-emotion	2106.14323	null	https://arxiv.org/abs/2106.14323v2	https://arxiv.org/pdf/2106.14323v2.pdf	Use of Variational Inference in Music Emotion Recognition	This work was developed aiming to employ Statistical techniques to the field of Music Emotion Recognition, a well-recognized area within the Signal Processing world, but hardly explored from the statistical point of view. Here, we opened several possibilities within the field, applying modern Bayesian Statistics techniques and developing efficient algorithms, focusing on the applicability of the results obtained. Although the motivation for this project was the development of a emotion-based music recommendation system, its main contribution is a highly adaptable multivariate model that can be useful interpreting any database where there is an interest in applying regularization in an efficient manner. Broadly speaking, we will explore what role a sound theoretical statistical analysis can play in the modeling of an algorithm that is able to understand a well-known database and what can be gained with this kind of approach.	['Hugo Tremonte de Carvalho', 'Nathalie Deziderio']	2021-06-27	null	null	null	null	['music-emotion-recognition']	['music']	[ 2.96088487e-01 -1.07810684e-01 2.37495258e-01 -1.66336104e-01 -4.11015630e-01 -3.13206315e-01 3.05801332e-01 1.73774332e-01 -6.35677457e-01 3.98866326e-01 2.38941163e-01 -1.21638149e-01 -7.20528185e-01 -6.83543265e-01 -3.45420778e-01 -8.83173704e-01 -1.07289441e-01 3.21945637e-01 8.19267146e-03 -2.92998105e-01 3.04132283e-01 7.58368254e-01 -1.91854072e+00 1.19874485e-01 4.60988492e-01 9.00323153e-01 1.62926123e-01 5.37232697e-01 -5.60569167e-02 3.75633717e-01 -5.37700891e-01 -3.26344579e-01 -2.94648875e-02 -7.24407196e-01 -5.62536836e-01 -1.33980450e-03 -3.34300876e-01 3.40548068e-01 2.82122254e-01 9.53922689e-01 4.71247822e-01 4.53384548e-01 7.97764122e-01 -5.79502821e-01 2.61683494e-01 6.38805389e-01 6.00774735e-02 7.06536025e-02 4.21177983e-01 -3.64601195e-01 8.54158998e-01 -4.75151598e-01 4.24216509e-01 9.37583685e-01 3.79395217e-01 1.01474859e-01 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-1.16904664e+00 2.68941909e-01 -1.95893794e-01 -5.87065101e-01 -4.43031698e-01 9.93386731e-02]	[15.799025535583496, 5.330885410308838]
307ce705-6239-4d92-a24d-f05ed3a9a100	deep-sketch-shape-hashing-with-segmented-3d	null	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.pdf	Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing	Sketch-based 3D shape retrieval has been extensively studied in recent works, most of which focus on improving the retrieval accuracy, whilst neglecting the efficiency. In this paper, we propose a novel framework for efficient sketch-based 3D shape retrieval, i.e., Deep Sketch-Shape Hashing (DSSH), which tackles the challenging problem from two perspectives. Firstly, we propose an intuitive 3D shape representation method to deal with unaligned shapes with arbitrary poses. Specifically, the proposed Segmented Stochastic-viewing Shape Network models discriminative 3D representations by a set of 2D images rendered from multiple views, which are stochastically selected from non-overlapping spatial segments of a 3D sphere. Secondly, Batch-Hard Binary Coding (BHBC) is developed to learn semantics-preserving compact binary codes by mining the hardest samples. The overall framework is jointly learned by developing an alternating iteration algorithm. Extensive experimental results on three benchmarks show that DSSH improves both the retrieval efficiency and accuracy remarkably, compared to the state-of-the-art methods.	[' Ling Shao', ' Jin Xie', ' Fumin Shen', ' Fan Zhu', ' Li Liu', ' Jie Qin', 'Jiaxin Chen']	2019-06-01	null	null	null	cvpr-2019-6	['3d-shape-retrieval', '3d-shape-representation']	['computer-vision', 'computer-vision']	[ 5.55988029e-02 -3.75736415e-01 -2.20148653e-01 -2.42012843e-01 -1.14231193e+00 -6.85085118e-01 6.94228709e-01 7.24804476e-02 -5.88788539e-02 2.37036161e-02 1.07557885e-01 -1.35157658e-02 -3.16003770e-01 -7.88572907e-01 -4.74991918e-01 -8.29328299e-01 1.10707887e-01 7.63626814e-01 1.94384143e-01 1.35293633e-01 5.50219297e-01 8.13037336e-01 -1.40288711e+00 8.77808705e-02 3.18138361e-01 1.26270759e+00 7.36245364e-02 1.54303432e-01 -2.68837482e-01 9.16677248e-03 -1.04845330e-01 -5.06130517e-01 4.06151474e-01 8.67935866e-02 -3.53559256e-01 1.23670459e-01 5.20102501e-01 -5.32433867e-01 -4.94049847e-01 8.84692729e-01 7.50651300e-01 5.88745214e-02 9.70243216e-01 -1.08855069e+00 -1.01997936e+00 -5.51435575e-02 -7.59096086e-01 -3.27893376e-01 3.26740414e-01 -1.47989333e-01 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c2adb14e-f565-469b-b67f-160891e1c4b7	neur2sp-neural-two-stage-stochastic	2205.12006	null	https://arxiv.org/abs/2205.12006v2	https://arxiv.org/pdf/2205.12006v2.pdf	Neur2SP: Neural Two-Stage Stochastic Programming	Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used class of stochastic programming models. Solving 2SPs exactly requires optimizing over an expected value function that is computationally intractable. Having a mixed-integer linear program (MIP) or a nonlinear program (NLP) in the second stage further aggravates the intractability, even when specialized algorithms that exploit problem structure are employed. Finding high-quality (first-stage) solutions -- without leveraging problem structure -- can be crucial in such settings. We develop Neur2SP, a new method that approximates the expected value function via a neural network to obtain a surrogate model that can be solved more efficiently than the traditional extensive formulation approach. Neur2SP makes no assumptions about the problem structure, in particular about the second-stage problem, and can be implemented using an off-the-shelf MIP solver. Our extensive computational experiments on four benchmark 2SP problem classes with different structures (containing MIP and NLP second-stage problems) demonstrate the efficiency (time) and efficacy (solution quality) of Neur2SP. In under 1.66 seconds, Neur2SP finds high-quality solutions across all problems even as the number of scenarios increases, an ideal property that is difficult to have for traditional 2SP solution techniques. Namely, the most generic baseline method typically requires minutes to hours to find solutions of comparable quality.	['Merve Bodur', 'Elias B. Khalil', 'Rahul Patel', 'Justin Dumouchelle']	2022-05-20	null	null	null	null	['decision-making-under-uncertainty', 'decision-making-under-uncertainty']	['medical', 'reasoning']	[ 2.47091115e-01 2.46280625e-01 -3.91914010e-01 -3.65789622e-01 -1.43546891e+00 -8.18542480e-01 -2.34845579e-01 9.46389958e-02 -2.28173986e-01 1.13862503e+00 -2.52766758e-01 -6.71319306e-01 -5.51585972e-01 -8.48658979e-01 -1.01171076e+00 -8.21169436e-01 -1.41673207e-01 8.57368350e-01 -8.91979188e-02 -5.88608161e-02 2.78115779e-01 1.78021386e-01 -1.22691464e+00 2.90608227e-01 1.17390442e+00 1.40725470e+00 2.97412008e-01 5.40950060e-01 -3.19297522e-01 4.68186289e-01 -4.94253337e-01 -5.11032403e-01 6.17202580e-01 1.86663583e-01 -6.88640118e-01 -4.99483980e-02 3.98238190e-03 -1.35375991e-01 1.04212962e-01 1.13008261e+00 3.77383798e-01 1.99732974e-01 3.79467785e-01 -1.51636147e+00 -2.39714459e-01 8.74395370e-01 -1.06110108e+00 1.61161181e-03 4.00710970e-01 4.36151117e-01 1.12492430e+00 -5.34805715e-01 3.31004709e-01 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-9.82144773e-01 -1.11967981e+00 -1.74232483e-01 -4.32847798e-01 -5.20836532e-01 2.56470591e-01 -6.14698887e-01 -5.05911767e-01 -5.08014500e-01]	[5.681464672088623, 3.560732841491699]
4187727f-9587-4471-8410-ef3481497ba6	deepproblog-neural-probabilistic-logic	1805.10872	null	http://arxiv.org/abs/1805.10872v2	http://arxiv.org/pdf/1805.10872v2.pdf	DeepProbLog: Neural Probabilistic Logic Programming	We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.	['Sebastijan Dumančić', 'Luc De Raedt', 'Angelika Kimmig', 'Robin Manhaeve', 'Thomas Demeester']	2018-05-28	deepproblog-neural-probabilistic-logic-1	http://papers.nips.cc/paper/7632-deepproblog-neural-probabilistic-logic-programming	http://papers.nips.cc/paper/7632-deepproblog-neural-probabilistic-logic-programming.pdf	neurips-2018-12	['program-induction']	['computer-code']	[-9.27130580e-02 6.32661343e-01 -7.48989284e-01 -7.30968356e-01 -5.97337544e-01 -4.41008866e-01 9.47668254e-01 2.23263994e-01 -1.62642762e-01 8.86344731e-01 1.39879048e-01 -7.12558508e-01 -3.87728244e-01 -1.47562373e+00 -1.10238194e+00 3.85204442e-02 -3.92338991e-01 1.19459581e+00 5.16218245e-01 -5.64788282e-02 -6.88712448e-02 5.65867364e-01 -1.65588975e+00 6.97421193e-01 4.73881006e-01 9.93293464e-01 -5.24642646e-01 6.80741668e-01 -4.77620512e-01 1.67558098e+00 -1.73053071e-01 -6.81223333e-01 -3.31154734e-01 4.05704111e-01 -9.85292971e-01 -8.13582778e-01 3.37053210e-01 -6.36582732e-01 -5.25906146e-01 9.37882900e-01 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78526357-edfb-4a90-9328-f37c8f1e01fb	loss-optimal-classification-trees-a	2306.00857	null	https://arxiv.org/abs/2306.00857v1	https://arxiv.org/pdf/2306.00857v1.pdf	Loss-Optimal Classification Trees: A Generalized Framework and the Logistic Case	The Classification Tree (CT) is one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in Mixer-Integer Programming (MIP) solvers, several exact formulations of the learning problem have been developed. In this paper, we argue that some of the most relevant ones among these training models can be encapsulated within a general framework, whose instances are shaped by the specification of loss functions and regularizers. Next, we introduce a novel realization of this framework: specifically, we consider the logistic loss, handled in the MIP setting by a linear piece-wise approximation, and couple it with $\ell_1$-regularization terms. The resulting Optimal Logistic Tree model numerically proves to be able to induce trees with enhanced interpretability features and competitive generalization capabilities, compared to the state-of-the-art MIP-based approaches.	['Matteo Lapucci', 'Tommaso Aldinucci']	2023-06-01	null	null	null	null	['interpretable-machine-learning']	['methodology']	[ 3.44446778e-01 6.17955685e-01 -4.61602181e-01 -4.89337355e-01 -7.67672718e-01 -4.70248789e-01 4.78502661e-01 4.36809868e-01 -1.82782635e-01 8.16695094e-01 -2.16551676e-01 -3.98828924e-01 -7.00681925e-01 -8.80246222e-01 -7.79942632e-01 -7.51192331e-01 -1.00206330e-01 7.85552919e-01 -2.14207023e-01 -1.22140452e-01 1.22337468e-01 5.24753630e-01 -1.66022825e+00 1.99872300e-01 1.35274220e+00 1.39353180e+00 -8.59018117e-02 2.04250827e-01 -4.02702838e-01 7.31680393e-01 -2.09060580e-01 -9.11664128e-01 2.30240151e-01 -9.30008367e-02 -1.11039388e+00 1.60666347e-01 2.11106673e-01 2.67760932e-01 2.98727751e-01 8.31420064e-01 5.41748591e-02 -1.92017570e-01 7.26532459e-01 -1.33324254e+00 -4.44886655e-01 6.93744600e-01 -3.70442390e-01 -1.76497459e-01 1.47423014e-01 -2.95940429e-01 1.43888175e+00 -7.58174062e-01 5.59900641e-01 1.28978145e+00 6.09187067e-01 2.73344249e-01 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50641a7b-b92a-459d-afd6-27dc849b003c	native-language-identification-with-user	null	null	https://aclanthology.org/D18-1395	https://aclanthology.org/D18-1395.pdf	Native Language Identification with User Generated Content	We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.	['Shuly Wintner', 'Gili Goldin', 'Ella Rabinovich']	2018-10-01	null	null	null	emnlp-2018-10	['native-language-identification']	['natural-language-processing']	[-4.3919215e-01 -2.4276318e-01 -6.5493196e-01 -2.0089450e-01 -8.4016526e-01 -1.0240122e+00 8.0849999e-01 4.5370755e-01 -8.6580014e-01 3.1732568e-01 6.0806793e-01 -6.8755096e-01 2.1870095e-01 -4.1007778e-01 -9.1615148e-02 -9.7605899e-02 -1.8031636e-02 -5.4844242e-02 -3.3462903e-01 -1.7180908e-01 2.0850372e-01 4.1312662e-01 -1.2064716e+00 -2.4691290e-01 1.1644158e+00 5.0959384e-01 1.0218896e-01 4.2956010e-01 -5.5716223e-01 7.2925985e-01 -5.1225305e-01 -7.3331672e-01 1.2051648e-01 -3.4398642e-01 -7.7944839e-01 -1.3574179e-01 6.9191277e-01 1.3667201e-01 -4.0336728e-01 1.3082479e+00 2.2987451e-01 -2.1455675e-02 1.0003225e+00 -6.0297370e-01 -1.0499675e+00 1.1353517e+00 -4.3795231e-01 7.3359925e-01 9.7693175e-01 -2.0663481e-02 1.0751621e+00 -9.9894351e-01 8.0125964e-01 1.5238844e+00 8.5477602e-01 1.8487167e-01 -1.0558636e+00 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abed8583-8e8d-4410-8282-92c07fd2035c	multi-target-embodied-question-answering	1904.04686	null	http://arxiv.org/abs/1904.04686v1	http://arxiv.org/pdf/1904.04686v1.pdf	Multi-Target Embodied Question Answering	Embodied Question Answering (EQA) is a relatively new task where an agent is asked to answer questions about its environment from egocentric perception. EQA makes the fundamental assumption that every question, e.g., "what color is the car?", has exactly one target ("car") being inquired about. This assumption puts a direct limitation on the abilities of the agent. We present a generalization of EQA - Multi-Target EQA (MT-EQA). Specifically, we study questions that have multiple targets in them, such as "Is the dresser in the bedroom bigger than the oven in the kitchen?", where the agent has to navigate to multiple locations ("dresser in bedroom", "oven in kitchen") and perform comparative reasoning ("dresser" bigger than "oven") before it can answer a question. Such questions require the development of entirely new modules or components in the agent. To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module. The program generator converts the given question into sequential executable sub-programs; the navigator guides the agent to multiple locations pertinent to the navigation-related sub-programs; and the controller learns to select relevant observations along its path. These observations are then fed to the VQA module to predict the answer. We perform detailed analysis for each of the model components and show that our joint model can outperform previous methods and strong baselines by a significant margin.	['Mohit Bansal', 'Tamara L. Berg', 'Licheng Yu', 'Dhruv Batra', 'Xinlei Chen', 'Georgia Gkioxari']	2019-04-09	multi-target-embodied-question-answering-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Multi-Target_Embodied_Question_Answering_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Multi-Target_Embodied_Question_Answering_CVPR_2019_paper.pdf	cvpr-2019-6	['embodied-question-answering']	['computer-vision']	[-7.95087963e-02 3.13081235e-01 2.84321100e-01 -4.62404281e-01 -7.30734587e-01 -1.03725529e+00 4.32272941e-01 1.52186483e-01 -4.08473074e-01 3.98534775e-01 1.87812373e-01 -7.44511068e-01 -2.05309868e-01 -1.09594452e+00 -8.43261540e-01 -4.54319656e-01 2.14444399e-01 8.27859759e-01 3.52632642e-01 -6.79532766e-01 3.54688078e-01 1.15056053e-01 -1.60889781e+00 2.10608169e-01 7.99543917e-01 5.58883846e-01 7.40398049e-01 9.15526152e-01 -1.29769057e-01 1.17447031e+00 -5.41647613e-01 -3.23687106e-01 -7.16559663e-02 -6.62585795e-01 -1.45908213e+00 -2.52201021e-01 2.36447811e-01 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f283d877-0e87-4d2f-9ba0-38b294fe4b56	information-extraction-from-documents	2304.10994	null	https://arxiv.org/abs/2304.10994v1	https://arxiv.org/pdf/2304.10994v1.pdf	Information Extraction from Documents: Question Answering vs Token Classification in real-world setups	Research in Document Intelligence and especially in Document Key Information Extraction (DocKIE) has been mainly solved as Token Classification problem. Recent breakthroughs in both natural language processing (NLP) and computer vision helped building document-focused pre-training methods, leveraging a multimodal understanding of the document text, layout and image modalities. However, these breakthroughs also led to the emergence of a new DocKIE subtask of extractive document Question Answering (DocQA), as part of the Machine Reading Comprehension (MRC) research field. In this work, we compare the Question Answering approach with the classical token classification approach for document key information extraction. We designed experiments to benchmark five different experimental setups : raw performances, robustness to noisy environment, capacity to extract long entities, fine-tuning speed on Few-Shot Learning and finally Zero-Shot Learning. Our research showed that when dealing with clean and relatively short entities, it is still best to use token classification-based approach, while the QA approach could be a good alternative for noisy environment or long entities use-cases.	['Fabien Caspani', 'William Vanhuffel', 'Joël Tang', 'Pirashanth Ratnamogan', 'Laurent Lam']	2023-04-21	null	null	null	null	['key-information-extraction', 'reading-comprehension', 'machine-reading-comprehension']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 5.93248010e-01 1.30585566e-01 1.50629148e-01 -1.51942849e-01 -1.13883853e+00 -6.56640589e-01 9.76379097e-01 8.53219867e-01 -5.29759228e-01 6.66646540e-01 3.96240801e-01 -3.28855842e-01 -3.94494087e-01 -8.83849740e-01 -3.81813198e-01 -5.20929217e-01 1.58685982e-01 5.30496061e-01 3.70382369e-01 -3.35343510e-01 8.22624564e-01 1.86547741e-01 -1.74446833e+00 6.52281880e-01 9.60439861e-01 9.20346558e-01 4.04980779e-01 1.32238197e+00 -9.00881052e-01 1.18500197e+00 -7.45945275e-01 -2.67284006e-01 -2.95682639e-01 -1.53397620e-01 -1.54897702e+00 -4.33438867e-02 4.75022286e-01 -5.80171682e-02 1.89050227e-01 7.26419210e-01 7.88634837e-01 2.74975389e-01 5.84935546e-01 -8.79068375e-01 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8adc749b-f97b-4c0e-b984-244b8f511a6c	improving-faithfulness-of-abstractive	2212.09726	null	https://arxiv.org/abs/2212.09726v1	https://arxiv.org/pdf/2212.09726v1.pdf	Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences	Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsistency can be caused by irrelevant parts of the input text, which act as confounders. To that end, we leverage information-theoretic measures of causal effects to quantify the amount of confounding and precisely quantify how they affect the summarization performance. Based on insights derived from our theoretical results, we design a simple multi-task model to control such confounding by leveraging human-annotated relevant sentences when available. Crucially, we give a principled characterization of data distributions where such confounding can be large thereby necessitating the use of human annotated relevant sentences to generate factual summaries. Our approach improves faithfulness scores by 20\% over strong baselines on AnswerSumm \citep{fabbri2021answersumm}, a conversation summarization dataset where lack of faithfulness is a significant issue due to the subjective nature of the task. Our best method achieves the highest faithfulness score while also achieving state-of-the-art results on standard metrics like ROUGE and METEOR. We corroborate these improvements through human evaluation.	['Asli Celikyilmaz', 'Scott Wen-tau Yih', 'Yashar Mehdad', 'Lili Yu', 'Haoran Li', 'Ankit Arun', 'Arash Einolghozati', 'Asish Ghoshal']	2022-12-19	null	null	null	null	['abstractive-text-summarization']	['natural-language-processing']	[ 3.49032789e-01 6.10221922e-01 -2.64642626e-01 -2.79950023e-01 -1.44233286e+00 -7.98367560e-01 1.02364218e+00 4.74056393e-01 -4.40885127e-01 1.13063467e+00 1.24612641e+00 -1.68750182e-01 -4.14774800e-03 -5.02464950e-01 -8.96900713e-01 -2.07410783e-01 1.61145285e-01 4.36568111e-01 6.01145066e-02 -4.99048799e-01 6.82318389e-01 -2.47039810e-01 -1.14429176e+00 5.88900030e-01 1.20984614e+00 1.94280341e-01 -7.96120465e-02 8.34076941e-01 5.01163341e-02 1.00824463e+00 -1.16952193e+00 -6.26734197e-01 -1.85710907e-01 -8.06266427e-01 -1.09729576e+00 -1.48139656e-01 7.51770496e-01 -1.37860924e-01 -1.28353730e-01 9.11228597e-01 6.86004281e-01 2.45265439e-02 9.39123094e-01 -9.68477666e-01 -4.15665030e-01 1.13314044e+00 -5.24796128e-01 5.67343473e-01 6.57718420e-01 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006f874f-b699-4d35-b956-dc41220b81df	early-guessing-for-dialect-identification	null	null	https://openreview.net/forum?id=mdAhC06ICCb	https://openreview.net/pdf?id=mdAhC06ICCb	Early Guessing for Dialect Identification	This paper deals with the problem of incremental dialect identification. Our goal is to reliably determine the dialect before the full utterance is given as input. The major part of the previous research on dialect identification has been model-centric with a focus on performance. We address a new question: How much input is needed to identify a dialect? Our approach is a data-centric analysis that results in general criteria for finding the shortest input needed to make a plausible guess. Working with two sets of dialects (Swiss German and Indo-Aryan languages), we show that the dialect can be identified well before the end of the input utterance. To determine the optimal point for making the first guess, we propose a heuristic that involves calibrated model confidence (temperature scaling) and input length. We show that the same input shortening criteria apply to both of our data sets. While the performance with the early guesses is still below the performance on the full input, the gap is smaller when the overall performance of the fine-tuned model is better.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['dialect-identification']	['natural-language-processing']	[ 1.68318957e-01 -3.36227603e-02 -1.82026699e-02 -7.72368789e-01 -1.05000985e+00 -1.06359780e+00 5.30335128e-01 1.36310548e-01 -4.24506605e-01 3.62479508e-01 2.27509186e-01 -8.02737236e-01 -2.98655108e-02 -4.08733398e-01 -2.21782446e-01 -5.69115639e-01 3.55818629e-01 9.27181184e-01 2.56130487e-01 -7.04680264e-01 4.81436163e-01 4.25604999e-01 -1.49071383e+00 1.26153573e-01 9.70121562e-01 3.36895168e-01 2.55349815e-01 1.03297007e+00 -2.16447890e-01 3.10617745e-01 -5.50443172e-01 -4.06840116e-01 3.15406054e-01 -8.73165905e-01 -1.46405470e+00 -1.81107651e-02 5.16544640e-01 -1.92453757e-01 6.82846606e-02 9.77503002e-01 5.49237847e-01 -1.01739891e-01 6.46566510e-01 -8.40973735e-01 -2.05449700e-01 1.08816576e+00 8.58723745e-02 7.44291767e-02 5.02963245e-01 2.02560294e-02 1.07296634e+00 -7.30162024e-01 4.85751241e-01 1.28147042e+00 6.62169576e-01 4.66924042e-01 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-1.00217128e+00 -5.55227041e-01 3.27746987e-01 5.31378567e-01 -1.97031677e-01 -3.50239903e-01 8.55946913e-02]	[14.192214012145996, 6.694639205932617]
60b595ba-e8bf-4dd2-bc67-555791dbb960	cloud-detection-algorithm-for-remote-sensing	1810.05782	null	http://arxiv.org/abs/1810.05782v1	http://arxiv.org/pdf/1810.05782v1.pdf	Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks	This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.	['Sorour Mohajerani', 'Thomas A. Krammer', 'Parvaneh Saeedi']	2018-10-13	null	null	null	null	['cloud-detection']	['computer-vision']	[ 2.23603576e-01 -3.90945852e-01 3.43862951e-01 -4.58865196e-01 -7.36671031e-01 -6.98147833e-01 3.02942544e-01 1.75883591e-01 -5.49039423e-01 7.66161919e-01 -5.97208977e-01 -6.22936249e-01 -6.70278817e-02 -1.33951890e+00 -5.99733949e-01 -1.01449013e+00 -1.96934640e-01 2.74391681e-01 1.41406478e-02 -2.29574228e-03 1.11906372e-01 8.80413473e-01 -1.83527744e+00 3.31706375e-01 1.29200518e+00 1.28541875e+00 3.22956562e-01 6.60088599e-01 -2.60915607e-01 6.37218118e-01 -3.48573893e-01 1.78446516e-01 6.44704878e-01 -1.85119063e-01 -5.80285847e-01 1.29018605e-01 9.66320157e-01 -6.69135034e-01 1.53466463e-01 1.26410937e+00 2.04885200e-01 -2.83809721e-01 6.70686901e-01 -8.25887978e-01 -1.20620005e-01 2.50704467e-01 -6.42225027e-01 3.92284878e-02 -5.54107249e-01 5.56868725e-02 8.36498618e-01 -1.15534997e+00 3.92398924e-01 5.66590786e-01 9.59575355e-01 -3.41152027e-02 -7.15944052e-01 -8.17719817e-01 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dd501312-096d-489b-8b0c-674f7cf7cfa8	comprehensive-fair-meta-learned-recommender	2206.04789	null	https://arxiv.org/abs/2206.04789v1	https://arxiv.org/pdf/2206.04789v1.pdf	Comprehensive Fair Meta-learned Recommender System	In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation scenarios, i.e. learning to learn the user preference by only a few past interaction items. The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively. They aim at deriving general knowledge across preference learning of various users, so as to rapidly adapt to the future new user with the learned prior and a small amount of training data. However, previous works have shown that recommender systems are generally vulnerable to bias and unfairness. Despite the success of meta-learning at improving the recommendation performance with cold-start, the fairness issues are largely overlooked. In this paper, we propose a comprehensive fair meta-learning framework, named CLOVER, for ensuring the fairness of meta-learned recommendation models. We systematically study three kinds of fairness - individual fairness, counterfactual fairness, and group fairness in the recommender systems, and propose to satisfy all three kinds via a multi-task adversarial learning scheme. Our framework offers a generic training paradigm that is applicable to different meta-learned recommender systems. We demonstrate the effectiveness of CLOVER on the representative meta-learned user preference estimator on three real-world data sets. Empirical results show that CLOVER achieves comprehensive fairness without deteriorating the overall cold-start recommendation performance.	['Jingrui He', 'Tianxin Wei']	2022-06-09	null	null	null	null	['general-knowledge']	['miscellaneous']	[-2.78698355e-01 -2.72142857e-01 -4.56001401e-01 -5.20979464e-01 -5.88650823e-01 -5.41456223e-01 3.94190431e-01 -3.52521598e-01 -4.71222669e-01 9.61847603e-01 2.21373707e-01 -1.36182696e-01 -3.23442787e-01 -7.21165299e-01 -3.93684208e-01 -8.90101969e-01 -1.99575983e-02 3.18139851e-01 -3.52078080e-01 -5.42053998e-01 3.81464303e-01 -5.13874777e-02 -1.39337885e+00 4.08947617e-01 1.49077809e+00 8.14849138e-01 -1.51386350e-01 5.93020201e-01 1.66396588e-01 5.43594122e-01 -4.85720783e-01 -8.78242314e-01 4.18770581e-01 -3.70968312e-01 -6.74106002e-01 -4.27688658e-01 3.95776689e-01 -4.68314290e-01 -2.74640501e-01 1.02191067e+00 8.35844696e-01 8.62236381e-01 6.12778544e-01 -1.49301481e+00 -9.62668657e-01 8.76657546e-01 -3.50488394e-01 -2.38917544e-02 1.01946011e-01 3.65766473e-02 1.19886160e+00 -7.50083387e-01 -7.68429190e-02 1.15838277e+00 5.75589657e-01 1.11161995e+00 -9.95442212e-01 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76f35f18-f6d2-4d96-9e88-aa287802e4a3	a-new-speech-feature-fusion-method-with-cross	2211.13377	null	https://arxiv.org/abs/2211.13377v1	https://arxiv.org/pdf/2211.13377v1.pdf	A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition	In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN). The Mel filter bank features (MFBFs) of different frequency resolutions can be extracted from each speech frame of a speaker's speech by several Mel filter banks, where the numbers of the triangular filters in the Mel filter banks are different. Due to the frequency resolutions of these MFBFs are different, there are some complementaries for these MFBFs. The CG-PCNN is utilized to extract the deep features from these MFBFs, which applies a cross gate mechanism to capture the complementaries for improving the performance of the speaker recognition system. Then, the fusion feature can be obtained by concatenating these deep features for speaker recognition. The experimental results show that the speaker recognition system with the proposed speech feature fusion method is effective, and marginally outperforms the existing state-of-the-art systems.	['Ye Zhang', 'Wenyi Yan', 'Jiacheng Zhang']	2022-11-24	null	null	null	null	['speaker-recognition']	['speech']	[-3.74202207e-02 -4.61917400e-01 1.61977708e-01 -3.89431566e-01 -5.63452721e-01 -6.54323101e-02 4.23654824e-01 -2.27804080e-01 -3.30745488e-01 2.42237538e-01 4.88514304e-01 -2.04298869e-01 1.67518675e-01 -6.15211248e-01 -2.63468593e-01 -9.81802046e-01 1.54567018e-01 -4.95096266e-01 2.22432166e-01 -3.83811831e-01 8.95562619e-02 6.65119588e-01 -1.78732967e+00 6.46306217e-01 5.48735738e-01 1.36273801e+00 2.22167507e-01 7.99238205e-01 -3.93805176e-01 3.56060743e-01 -9.93778765e-01 -1.51584670e-01 -7.04822456e-03 -3.36376280e-01 -4.21470672e-01 -5.82496822e-02 7.22924918e-02 -4.52741474e-01 -8.23217928e-01 1.24021840e+00 7.95806348e-01 3.83047462e-01 4.46817070e-01 -9.53100681e-01 -5.50925612e-01 8.60145390e-01 -2.30474874e-01 4.83758688e-01 3.61474156e-01 -1.96528718e-01 7.45023906e-01 -1.17589200e+00 -2.41657019e-01 1.83996236e+00 4.74935889e-01 4.94544059e-01 -7.33500302e-01 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-6.28953934e-01 -1.87252648e-02 8.88545990e-01 -4.60420549e-01 -5.10326743e-01 1.15135215e-01]	[14.466071128845215, 6.005220413208008]
b8ac3bbd-52cf-4e0e-ad3e-af4c0c3a8ea7	data-structures-for-density-estimation	2306.11312	null	https://arxiv.org/abs/2306.11312v1	https://arxiv.org/pdf/2306.11312v1.pdf	Data Structures for Density Estimation	We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is "close" to $p$. Our main result is the first data structure that, given a sublinear (in $n$) number of samples from $p$, identifies $v_i$ in time sublinear in $k$. We also give an improved version of the algorithm of Acharya et al. (2018) that reports $v_i$ in time linear in $k$. The experimental evaluation of the latter algorithm shows that it achieves a significant reduction in the number of operations needed to achieve a given accuracy compared to prior work.	['Sandeep Silwal', 'Shyam Narayanan', 'Piotr Indyk', 'Justin Y. Chen', 'Alexandr Andoni', 'Anders Aamand']	2023-06-20	null	null	null	null	['density-estimation']	['methodology']	[-6.06724441e-01 -9.33910161e-02 -2.53944755e-01 -3.55681390e-01 -1.44705427e+00 -9.00255620e-01 -1.17961861e-01 3.73491973e-01 -6.77474916e-01 9.87663805e-01 -7.98486054e-01 -6.50370538e-01 -3.68858516e-01 -1.04937494e+00 -1.12201953e+00 -5.72460175e-01 -5.57317257e-01 1.04219103e+00 1.20124593e-01 3.69438142e-01 4.52809691e-01 2.97553539e-01 -1.30610025e+00 -7.77593136e-01 7.40958691e-01 1.66734219e+00 -1.23255022e-01 7.86519706e-01 -1.28653914e-01 3.17539603e-01 -5.87917626e-01 -7.20696926e-01 4.13357049e-01 -1.09646186e-01 -8.40454817e-01 -3.30655903e-01 3.50256234e-01 -5.57650745e-01 -3.45737964e-01 1.48100770e+00 2.34813899e-01 1.12959877e-01 9.30441916e-01 -1.25769508e+00 -4.70911920e-01 1.09561980e+00 -1.19222152e+00 4.12260771e-01 9.34688225e-02 1.96268503e-02 9.78725374e-01 -5.96746802e-01 4.39417481e-01 1.08952463e+00 5.70856810e-01 8.33977982e-02 -1.77869689e+00 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0d6a4af6-e23c-46dd-89e2-fa14fd414e46	single-image-hdr-reconstruction-by-multi	2210.15897	null	https://arxiv.org/abs/2210.15897v1	https://arxiv.org/pdf/2210.15897v1.pdf	Single-Image HDR Reconstruction by Multi-Exposure Generation	High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a perfect solution due to ghosting and other visual artifacts in the reconstruction. Recent attempts at single-image HDR reconstruction show a promising alternative: by learning to map pixel values to their irradiance using a neural network, one can bypass the align-and-merge pipeline completely yet still obtain a high-quality HDR image. In this work, we propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction via learning to generate multiple exposures from a single image. Our neural network can invert the camera response to reconstruct pixel irradiance before synthesizing multiple exposures and hallucinating details in under- and over-exposed regions from a single input image. To train the network, we propose a representation loss, a reconstruction loss, and a perceptual loss applied on pairs of under- and over-exposure images and thus do not require HDR images for training. Our experiments show that our proposed model can effectively reconstruct HDR images. Our qualitative and quantitative results show that our method achieves state-of-the-art performance on the DrTMO dataset. Our code is available at https://github.com/VinAIResearch/single_image_hdr.	['Binh-Son Hua', 'Rang Nguyen', 'Quynh Le', 'Phuoc-Hieu Le']	2022-10-28	null	null	null	null	['hdr-reconstruction', 'tone-mapping']	['computer-vision', 'computer-vision']	[ 6.60233855e-01 -8.63396898e-02 1.40883714e-01 -3.63511473e-01 -9.00165856e-01 -2.40368783e-01 4.15356547e-01 -6.01781249e-01 -2.99809724e-01 6.36292696e-01 1.25133500e-01 -1.64244547e-01 2.52896607e-01 -8.32183778e-01 -1.01593494e+00 -8.05817366e-01 4.50249583e-01 -1.34663552e-01 2.95354240e-02 -2.03256890e-01 9.13760290e-02 4.68123019e-01 -1.55007672e+00 2.79350281e-01 9.34853613e-01 9.42272425e-01 6.01457417e-01 9.44129765e-01 2.76845694e-01 1.22499323e+00 -5.20310163e-01 -2.68078893e-01 7.67154098e-01 -7.10430026e-01 -4.94041204e-01 3.63764167e-01 7.71388054e-01 -9.84584153e-01 -9.26839709e-01 1.00695968e+00 6.80535734e-01 5.50216846e-02 2.09413514e-01 -8.81084621e-01 -1.03922522e+00 1.78639218e-01 -7.35919058e-01 -6.79374710e-02 4.71807063e-01 5.43131411e-01 6.98104680e-01 -8.19477677e-01 5.02044559e-01 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12c1a8c3-f445-4419-a385-6a04c90bf43d	a-survey-of-identification-and-mitigation-of	2210.04491	null	https://arxiv.org/abs/2210.04491v1	https://arxiv.org/pdf/2210.04491v1.pdf	A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis	The problem of algorithmic bias in machine learning has gained a lot of attention in recent years due to its concrete and potentially hazardous implications in society. In much the same manner, biases can also alter modern industrial and safety-critical applications where machine learning are based on high dimensional inputs such as images. This issue has however been mostly left out of the spotlight in the machine learning literature. Contrarily to societal applications where a set of proxy variables can be provided by the common sense or by regulations to draw the attention on potential risks, industrial and safety-critical applications are most of the times sailing blind. The variables related to undesired biases can indeed be indirectly represented in the input data, or can be unknown, thus making them harder to tackle. This raises serious and well-founded concerns towards the commercial deployment of AI-based solutions, especially in a context where new regulations clearly address the issues opened by undesired biases in AI. Consequently, we propose here to make an overview of recent advances in this area, firstly by presenting how such biases can demonstrate themselves, then by exploring different ways to bring them to light, and by probing different possibilities to mitigate them. We finally present a practical remote sensing use-case of industrial Fairness.	['Jean-Michel Loubes', 'Lucas Hervier', 'Agustin Picard', 'Laurent Risser']	2022-10-10	null	null	null	null	['common-sense-reasoning']	['reasoning']	[ 8.28924716e-01 4.53049719e-01 -3.09382200e-01 -3.87161553e-01 -2.93220699e-01 -6.09088898e-01 8.30423057e-01 2.10607126e-01 -6.45720780e-01 1.04466939e+00 3.52657884e-02 -4.65939552e-01 -6.46200657e-01 -9.61709678e-01 -5.77512205e-01 -1.06308877e+00 2.22093031e-01 2.15962902e-01 -3.22616994e-01 -3.24015290e-01 5.19334614e-01 5.77047884e-01 -1.66857028e+00 -2.66046990e-02 7.24777937e-01 1.01476443e+00 -1.01693548e-01 1.47142321e-01 1.80193912e-02 3.84695053e-01 -7.08576024e-01 -6.95347071e-01 4.85255331e-01 -3.43867809e-01 -6.35782897e-01 -8.97957385e-02 1.67565614e-01 1.24052301e-01 3.02675128e-01 1.35241652e+00 5.36132336e-01 -1.35154545e-01 6.82407260e-01 -1.29309726e+00 -6.87471569e-01 5.48883379e-01 -6.06304526e-01 -1.71180874e-01 4.75128228e-03 4.27693337e-01 8.87699366e-01 -2.81241566e-01 2.80571014e-01 1.34057617e+00 3.42190444e-01 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-7.13621795e-01 -5.85686445e-01 -5.39003849e-01 2.98861772e-01 4.50188458e-01 -7.67637551e-01 -5.30882657e-01 -1.54574469e-01]	[8.75290584564209, 5.153059959411621]
d623fcbf-5244-4f4c-a0f4-fdba24bfd0cf	event-extraction-based-on-open-information	1907.00692	null	https://arxiv.org/abs/1907.00692v1	https://arxiv.org/pdf/1907.00692v1.pdf	Event extraction based on open information extraction and ontology	The work presented in this master thesis consists of extracting a set of events from texts written in natural language. For this purpose, we have based ourselves on the basic notions of the information extraction as well as the open information extraction. First, we applied an open information extraction(OIE) system for the relationship extraction, to highlight the importance of OIEs in event extraction, and we used the ontology to the event modeling. We tested the results of our approach with test metrics. As a result, the two-level event extraction approach has shown good performance results but requires a lot of expert intervention in the construction of classifiers and this will take time. In this context we have proposed an approach that reduces the expert intervention in the relation extraction, the recognition of entities and the reasoning which are automatic and based on techniques of adaptation and correspondence. Finally, to prove the relevance of the extracted results, we conducted a set of experiments using different test metrics as well as a comparative study.	['Sihem Sahnoun']	2019-06-24	null	null	null	null	['open-information-extraction']	['natural-language-processing']	[ 2.24635825e-01 5.73791981e-01 4.16292787e-01 -2.56668389e-01 -1.97175786e-01 -2.45075092e-01 9.76871729e-01 8.31137002e-01 -7.02935219e-01 8.89162898e-01 7.31287673e-02 -1.40094057e-01 -6.30628407e-01 -1.11920679e+00 -2.66089529e-01 -2.28023395e-01 -1.80503666e-01 5.56606650e-01 6.37735367e-01 -2.59309441e-01 2.51618594e-01 6.29431963e-01 -1.97234035e+00 2.85030961e-01 5.30908585e-01 6.23664081e-01 -4.93179522e-02 4.75347728e-01 -5.30584574e-01 9.43503857e-01 -7.45785296e-01 -2.49146253e-01 -1.72644943e-01 -5.20085931e-01 -1.20979476e+00 -1.57734349e-01 -4.72390145e-01 1.84933960e-01 4.03182894e-01 7.53207386e-01 3.23646814e-01 1.92503497e-01 9.07587647e-01 -1.18599594e+00 3.28250796e-01 8.70270252e-01 7.46182427e-02 9.16844979e-02 8.09803545e-01 -3.03162932e-01 6.05374217e-01 -6.75006151e-01 1.05880797e+00 8.55333269e-01 3.83635819e-01 1.67464837e-01 -8.24072778e-01 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6.10577106e-01 -4.28531170e-01 -6.49357915e-01 -2.78047830e-01 8.04498643e-02]	[9.301643371582031, 8.64735221862793]
9f7a8be5-3bff-4bea-b395-33d117885626	an-unsupervised-approach-to-user-simulation	null	null	https://aclanthology.org/W12-1606	https://aclanthology.org/W12-1606.pdf	An Unsupervised Approach to User Simulation: Toward Self-Improving Dialog Systems	null	['Sungjin Lee', 'Maxine Eskenazi']	2012-07-01	null	null	null	ws-2012-7	['user-simulation']	['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 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48546e7b-fa90-499c-b818-2a25487bea6a	modeling-cell-size-distribution-with	2304.06631	null	https://arxiv.org/abs/2304.06631v2	https://arxiv.org/pdf/2304.06631v2.pdf	Modeling Cell Size Distribution with Heterogeneous Flux Balance Analysis	For over two decades, Flux Balance Analysis (FBA) has been successfully used for predicting growth rates and intracellular reaction rates in microbiological metabolism. An aspect that is often omitted from this analysis, is segregation or heterogeneity between different cells. In this work, we propose an extended FBA method to model cell size distributions in balanced growth conditions. Hereto, a mathematical description of the concept of balanced growth in terms of cell mass distribution is presented. The cell mass distribution, quantified by the Number Density Function (NDF), is affected by cell growth and cell division. An optimization program is formulated in a general manner in which the NDF, average cell culture growth rate and reaction rates per cell mass are treated as optimization variables. As qualitative proof of concept, the methodology is illustrated on a core carbon model of Escherichia coli under aerobic growth conditions. This illustrates feasibility and applications of this method, while indicating some shortcomings intrinsic to the simplified biomass structuring and the time invariant approach.	['Steffen Waldherr', 'Florence H. Vermeire', 'Michiel Busschaert']	2023-04-13	null	null	null	null	['culture']	['speech']	[ 2.64103502e-01 -2.20212951e-01 1.25558889e-02 6.13370776e-01 6.51626348e-01 -6.15900218e-01 4.74272668e-01 6.79877281e-01 -3.58620942e-01 1.32878733e+00 -3.39511067e-01 -3.48844320e-01 -2.13743821e-01 -8.56267035e-01 -3.55938166e-01 -1.32575881e+00 -1.14640696e-02 4.02398914e-01 -8.72379765e-02 -3.88442948e-02 2.34580040e-01 9.25560594e-01 -1.52592134e+00 -4.17152524e-01 5.68667293e-01 8.74533057e-01 7.68301368e-01 9.12905514e-01 -2.68360287e-01 5.52387655e-01 -5.86313188e-01 1.60803705e-01 -7.41364956e-02 -7.79681683e-01 -6.25034332e-01 2.45111510e-01 -8.01026642e-01 3.46950561e-01 3.07264119e-01 6.85106337e-01 3.28794897e-01 2.63228893e-01 1.01040542e+00 -1.11929715e+00 9.68578309e-02 1.42169148e-01 -1.19189374e-01 1.25055000e-01 1.50578246e-01 1.63401440e-01 3.17232311e-01 -6.77808881e-01 5.21671772e-01 1.08712351e+00 3.99070472e-01 3.56284678e-01 -1.45277262e+00 1.19808167e-01 -1.01320948e-02 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545350cd-142a-4d4e-bf72-726f815e6e2f	implementation-of-an-internet-of-things	2203.12787	null	https://arxiv.org/abs/2203.12787v2	https://arxiv.org/pdf/2203.12787v2.pdf	Design of an Internet of Things System for Smart Hospitals	With the fast advancement of smart devices and Internet of Things (IoT) technologies, certain established situations are opening up new avenues of exploration. Particularly in the sphere of healthcare, the diverse and big population, the complicated and professional data, and the stringent environmental requirements for certain medical scenes and equipment all impose exceptionally high standards on hospital administration. As a result, an effective and secure Internet of things system is critical. This article proposes an IoT system that might be used in hospitals for a variety of purposes. This system collects data by LoRa, Wi-Fi, and other ways, uploads it to a cloud platform for processing over a secure connection, and then feeds it back to users in real-time via the user interface. This system enables precise indoor localization through the use of UWB, ECG signal detection, environmental monitoring, and data on people flow.	['Zihuai Lin', 'Xucun Yan', 'Jichao Leng']	2022-03-24	null	null	null	null	['indoor-localization']	['computer-vision']	[ 6.99614640e-04 -3.08390111e-01 -2.79099029e-02 -3.30198228e-01 -7.96993896e-02 -3.08086365e-01 1.17186807e-01 3.59877139e-01 -3.39185685e-01 1.01094341e+00 9.84101593e-02 -4.72516119e-01 -6.32934332e-01 -1.15272033e+00 3.01731288e-01 -8.12160969e-01 -1.24403484e-01 1.64210007e-01 -3.32319736e-03 9.55075771e-03 -2.00045884e-01 5.15010178e-01 -1.02036738e+00 -1.02841236e-01 6.39490604e-01 1.27691615e+00 8.16939585e-03 1.58203304e-01 -6.10869788e-02 1.66709721e-01 -6.74274564e-01 -9.16095823e-02 6.82823434e-02 -1.75893724e-01 -3.66562456e-01 -3.94157112e-01 -8.39905858e-01 -3.22729230e-01 -7.91333169e-02 7.12188303e-01 1.18900847e+00 -2.80154973e-01 -9.66073945e-02 -7.88531184e-01 -8.93722773e-02 6.52141511e-01 -1.10725120e-01 1.38256988e-02 6.98938608e-01 -6.37287507e-03 -4.77596857e-02 -2.15617076e-01 9.45424736e-02 6.71464026e-01 6.79751098e-01 8.50157216e-02 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-7.15166628e-01 -9.09312308e-01 4.90910970e-02 1.93087265e-01 -2.44566575e-01 1.34802964e-02 -2.62166202e-01]	[6.545906066894531, 0.916495144367218]
3edb2550-939f-4b69-a7fc-467736e16335	fristograms-revealing-and-exploiting-light	2107.10563	null	https://arxiv.org/abs/2107.10563v1	https://arxiv.org/pdf/2107.10563v1.pdf	Fristograms: Revealing and Exploiting Light Field Internals	In recent years, light field (LF) capture and processing has become an integral part of media production. The richness of information available in LFs has enabled novel applications like post-capture depth-of-field editing, 3D reconstruction, segmentation and matting, saliency detection, object detection and recognition, and mixed reality. The efficacy of such applications depends on certain underlying requirements, which are often ignored. For example, some operations such as noise-reduction, or hyperfan-filtering are only possible if a scene point Lambertian radiator. Some other operations such as the removal of obstacles or looking behind objects are only possible if there is at least one ray capturing the required scene point. Consequently, the ray distribution representing a certain scene point is an important characteristic for evaluating processing possibilities. The primary idea in this paper is to establish a relation between the capturing setup and the rays of the LF. To this end, we discretize the view frustum. Traditionally, a uniform discretization of the view frustum results in voxels that represents a single sample on a regularly spaced, 3-D grid. Instead, we use frustum-shaped voxels (froxels), by using depth and capturing-setup dependent discretization of the view frustum. Based on such discretization, we count the number of rays mapping to the same pixel on the capturing device(s). By means of this count, we propose histograms of ray-counts over the froxels (fristograms). Fristograms can be used as a tool to analyze and reveal interesting aspects of the underlying LF, like the number of rays originating from a scene point and the color distribution of these rays. As an example, we show its ability by significantly reducing the number of rays which enables noise reduction while maintaining the realistic rendering of non-Lambertian or partially occluded regions.	['Robin Kremer', 'Tobias Lange', 'Kelvin Chelli', 'Thorsten Herfet']	2021-07-22	null	null	null	null	['image-matting']	['computer-vision']	[ 5.62931597e-01 -3.69084597e-01 3.10326159e-01 -1.41925484e-01 -1.29533783e-01 -4.65084255e-01 6.22643828e-01 5.64619243e-01 -4.24234748e-01 5.86729407e-01 -2.45954946e-01 -1.30138472e-01 -1.64949313e-01 -1.21023273e+00 -6.95910633e-01 -6.86945200e-01 1.31379068e-01 3.74882817e-01 5.10546625e-01 -6.74113929e-02 3.36014390e-01 1.15475953e+00 -2.01426268e+00 2.27607176e-01 8.56626570e-01 9.50770438e-01 4.63880420e-01 5.69366276e-01 -6.06117606e-01 3.55811805e-01 -5.67601025e-01 -4.17600647e-02 1.95606396e-01 -4.18907046e-01 -3.09428513e-01 2.88249791e-01 2.96206474e-01 -2.71053493e-01 2.19860718e-01 1.21754146e+00 1.40691385e-01 1.94063634e-01 6.76862657e-01 -9.49806392e-01 1.67523369e-01 -3.39451544e-02 -5.82135320e-01 9.94417910e-03 6.20607138e-01 -1.35947345e-02 4.54693675e-01 -8.19996357e-01 6.95245028e-01 9.17290807e-01 2.24156216e-01 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9b5386ec-5157-4616-bcb6-f764178cbf44	action-machine-rethinking-action-recognition	1812.05770	null	http://arxiv.org/abs/1812.05770v2	http://arxiv.org/pdf/1812.05770v2.pdf	Action Machine: Rethinking Action Recognition in Trimmed Videos	Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit the scenes and objects. In this work, we present a conceptually simple, general and high-performance framework for action recognition in trimmed videos, aiming at person-centric modeling. The method, called Action Machine, takes as inputs the videos cropped by person bounding boxes. It extends the Inflated 3D ConvNet (I3D) by adding a branch for human pose estimation and a 2D CNN for pose-based action recognition, being fast to train and test. Action Machine can benefit from the multi-task training of action recognition and pose estimation, the fusion of predictions from RGB images and poses. On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97.2% and 94.3% on cross-view and cross-subject respectively. Action Machine also achieves competitive performance on another three smaller action recognition datasets: Northwestern UCLA Multiview Action3D, MSR Daily Activity3D and UTD-MHAD. Code will be made available.	['Jun-Jie Huang', 'Dalong Du', 'Wei Zou', 'Liang Xu', 'Manyu Chang', 'Guan Huang', 'Yiming Hu', 'Zheng Zhu', 'Jiagang Zhu']	2018-12-14	null	null	null	null	['multimodal-activity-recognition']	['computer-vision']	[ 7.50279948e-02 3.48573946e-03 -2.86227196e-01 -3.83396775e-01 -8.47346544e-01 -1.61771268e-01 5.15905142e-01 -7.56707370e-01 -4.11215305e-01 4.46197540e-01 6.77671492e-01 3.60797405e-01 4.04617071e-01 -1.97620764e-01 -8.33337307e-01 -6.02912664e-01 -8.24441016e-02 5.92821598e-01 3.08543533e-01 1.11195289e-01 -2.43312314e-01 4.52790320e-01 -1.60398173e+00 5.53356290e-01 1.16681464e-01 1.21778083e+00 -2.28785068e-01 8.69049072e-01 5.41174293e-01 1.05021560e+00 -3.78221840e-01 -3.74208361e-01 5.89785933e-01 -3.81087273e-01 -7.64019012e-01 7.73856819e-01 8.44380975e-01 -9.92635727e-01 -7.62973070e-01 3.11660558e-01 7.31933773e-01 8.38895515e-02 4.53240097e-01 -1.38418651e+00 -2.52629608e-01 -1.47805382e-02 -6.42322421e-01 1.57949731e-01 9.51497793e-01 4.42221433e-01 4.19743389e-01 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72dc129d-b56c-487d-bd36-a2d1b78e34d8	context-sensitive-malicious-spelling-error	1901.07688	null	http://arxiv.org/abs/1901.07688v1	http://arxiv.org/pdf/1901.07688v1.pdf	Context-Sensitive Malicious Spelling Error Correction	Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection. In this paper, we focus on malicious spelling correction, which requires an approach that relies on the context and the surface forms of targeted keywords. In the context of two applications--profanity detection and email spam detection--we show that malicious misspellings seriously degrade their performance. We then propose a context-sensitive approach for malicious spelling correction using word embeddings and demonstrate its superior performance compared to state-of-the-art spell checkers.	['Suma Bhat', 'Pramod Viswanath', 'Yuchen Li', 'Hongyu Gong']	2019-01-23	null	null	null	null	['spam-detection']	['natural-language-processing']	[ 6.48470521e-01 -4.87539470e-01 2.86449846e-02 -1.16697364e-02 -4.09779310e-01 -8.03862989e-01 8.70452464e-01 5.59127927e-01 -5.43604314e-01 5.64351916e-01 -1.17090754e-01 -7.67579854e-01 2.96959039e-02 -6.16643608e-01 -5.34954727e-01 -3.35856974e-01 3.48414093e-01 1.96788818e-01 5.93165755e-01 -5.50288379e-01 1.09452951e+00 6.91420019e-01 -1.21137309e+00 2.89345056e-01 8.92412126e-01 1.75679207e-01 -1.77640691e-01 1.11141443e+00 -4.79359508e-01 5.09564221e-01 -1.00904214e+00 -8.89103591e-01 8.67568702e-03 -3.42992097e-01 -6.18964136e-01 -2.61547744e-01 6.62412465e-01 -3.61345187e-02 -7.10325658e-01 1.65501916e+00 8.81625265e-02 -7.01983869e-02 5.35482347e-01 -1.28133976e+00 -8.55029881e-01 3.75432014e-01 -3.28350842e-01 3.28207016e-01 3.42243493e-01 1.50975972e-01 8.06796432e-01 -7.51421928e-01 6.46350920e-01 1.47706592e+00 6.62281692e-01 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06396515-3774-40ac-995b-a13a055db7c7	preserving-commonsense-knowledge-from-pre	2306.10790	null	https://arxiv.org/abs/2306.10790v1	https://arxiv.org/pdf/2306.10790v1.pdf	Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference	Fine-tuning has been proven to be a simple and effective technique to transfer the learned knowledge of Pre-trained Language Models (PLMs) to downstream tasks. However, vanilla fine-tuning easily overfits the target data and degrades the generalization ability. Most existing studies attribute it to catastrophic forgetting, and they retain the pre-trained knowledge indiscriminately without identifying what knowledge is transferable. Motivated by this, we frame fine-tuning into a causal graph and discover that the crux of catastrophic forgetting lies in the missing causal effects from the pretrained data. Based on the causal view, we propose a unified objective for fine-tuning to retrieve the causality back. Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs. Therefore, our method is flexible and can mitigate negative transfer while preserving knowledge. Since endowing models with commonsense is a long-standing challenge, we implement our method on commonsense QA with a proposed heuristic estimation to verify its effectiveness. In the experiments, our method outperforms state-of-the-art fine-tuning methods on all six commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models.	['Haibin Chen', 'Xichen Shang', 'Huawen Feng', 'Junlong Liu', 'Peitian Ma', 'Yue Wu', 'Shengjie Qiu', 'Qianli Ma', 'Junhao Zheng']	2023-06-19	null	null	null	null	['causal-inference', 'causal-inference']	['knowledge-base', 'miscellaneous']	[ 6.39940798e-02 2.02745453e-01 -2.22774237e-01 -2.94390500e-01 -4.75899696e-01 -6.85712039e-01 6.17539048e-01 -1.00982273e-02 -3.52679342e-01 9.54339802e-01 5.76225877e-01 -2.18351245e-01 -1.61918759e-01 -1.08939970e+00 -1.09277534e+00 -6.13438666e-01 3.00733358e-01 5.06258428e-01 2.25706622e-01 -6.42563283e-01 1.13813289e-01 5.78538515e-02 -1.06817698e+00 2.94289321e-01 1.53832805e+00 6.38880134e-01 3.24326783e-01 3.83593654e-03 -1.78271666e-01 9.37082529e-01 -5.40831625e-01 -8.93552125e-01 -1.61328211e-01 -4.48719114e-01 -1.05869639e+00 -5.59164107e-01 3.40246081e-01 -4.33020890e-01 -4.77828920e-01 1.15389466e+00 3.58362198e-01 2.19376355e-01 6.76876903e-01 -1.15250075e+00 -1.62517118e+00 1.20453393e+00 -1.68350965e-01 2.98101276e-01 3.28847244e-02 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8248c89c-7e52-4710-b31b-7149006016e3	joint-optimization-of-masks-and-deep	1502.04149	null	http://arxiv.org/abs/1502.04149v4	http://arxiv.org/pdf/1502.04149v4.pdf	Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation	Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including monaural speech separation, monaural singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30--4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30--2.48 dB GNSDR gain and 4.32--5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.	['Mark Hasegawa-Johnson', 'Po-Sen Huang', 'Paris Smaragdis', 'Minje Kim']	2015-02-13	null	null	null	null	['speech-denoising']	['speech']	[ 1.55394018e-01 -3.52608979e-01 3.43574643e-01 -5.47750629e-02 -1.33804667e+00 -5.16739070e-01 1.60295591e-01 -2.46312767e-01 -2.44761840e-01 4.67516512e-01 4.11163270e-01 -3.20349067e-01 2.96116639e-02 -5.01033701e-02 -4.86251503e-01 -9.78513956e-01 3.09229881e-01 -1.58182532e-01 -1.27579048e-01 -2.96922326e-01 -2.40756556e-01 2.83743888e-01 -1.69413829e+00 9.30830836e-02 1.14696479e+00 1.27622914e+00 4.70275104e-01 9.57607627e-01 2.86399066e-01 4.45863932e-01 -7.63478518e-01 -7.45633692e-02 3.86187106e-01 -7.54047930e-01 -1.55811593e-01 -1.75494194e-01 5.46265483e-01 -9.98163670e-02 -4.15438265e-01 1.14809787e+00 1.25167561e+00 4.50486243e-01 6.09388232e-01 -6.41694009e-01 -4.94567931e-01 6.83731556e-01 -3.61107826e-01 1.90123171e-01 -8.58741701e-02 1.43377990e-01 1.00570595e+00 -7.66155005e-01 -3.59282762e-01 1.11438894e+00 4.94548500e-01 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46b189c1-9d4a-4b75-8ce8-e10fc1ef35bb	tgdm-target-guided-dynamic-mixup-for-cross	2210.05392	null	https://arxiv.org/abs/2210.05392v2	https://arxiv.org/pdf/2210.05392v2.pdf	TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning	Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio. To better transfer the knowledge, the proposed Mixup-3T network contains three branches with shared parameters for classifying classes in the source domain, target domain, and intermediate domain. To generate the optimal intermediate domain, the DRGN learns to generate an optimal mix ratio according to the performance on auxiliary target data. Then, the whole TGDM framework is trained via bi-level meta-learning so that TGDM can rectify itself to achieve optimal performance on target data. Extensive experimental results on several benchmark datasets verify the effectiveness of our method.	['Yu-Gang Jiang', 'Yixin Cao', 'Jingjing Chen', 'Yuqian Fu', 'Linhai Zhuo']	2022-10-11	null	null	null	null	['cross-domain-few-shot', 'cross-domain-few-shot-learning']	['computer-vision', 'computer-vision']	[ 5.18494010e-01 1.48463309e-01 -4.76177245e-01 -2.71497101e-01 -8.83165061e-01 -2.92675674e-01 5.27571142e-01 -4.06049579e-01 -3.81927639e-02 6.11348271e-01 -6.13814443e-02 2.61457134e-02 1.35257438e-01 -1.08832598e+00 -6.21041000e-01 -6.68144643e-01 5.03376484e-01 5.56601048e-01 6.41340554e-01 -2.37889305e-01 4.53654118e-02 6.18534647e-02 -1.45642292e+00 6.09968603e-01 1.25561571e+00 1.28244913e+00 5.51119924e-01 1.15766764e-01 -4.68965143e-01 9.55118179e-01 -5.93045890e-01 -1.20376639e-01 5.37623167e-01 -7.78867364e-01 -6.21532798e-01 2.09449202e-01 1.43145293e-01 -4.00317460e-01 -1.50125936e-01 9.35979903e-01 5.69148898e-01 4.08893913e-01 7.83660889e-01 -1.58112931e+00 -8.62516463e-01 6.26577377e-01 -4.14989620e-01 1.87504724e-01 -1.80282276e-02 5.25786817e-01 6.44753456e-01 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ee491200-d50b-481f-9fc5-1cc7642f6f15	fc2rn-a-fully-convolutional-corner-refinement	2007.05113	null	https://arxiv.org/abs/2007.05113v1	https://arxiv.org/pdf/2007.05113v1.pdf	FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection	Recent scene text detection works mainly focus on curve text detection. However, in real applications, the curve texts are more scarce than the multi-oriented ones. Accurate detection of multi-oriented text with large variations of scales, orientations, and aspect ratios is of great significance. Among the multi-oriented detection methods, direct regression for the geometry of scene text shares a simple yet powerful pipeline and gets popular in academic and industrial communities, but it may produce imperfect detections, especially for long texts due to the limitation of the receptive field. In this work, we aim to improve this while keeping the pipeline simple. A fully convolutional corner refinement network (FC2RN) is proposed for accurate multi-oriented text detection, in which an initial corner prediction and a refined corner prediction are obtained at one pass. With a novel quadrilateral RoI convolution operation tailed for multi-oriented scene text, the initial quadrilateral prediction is encoded into the feature maps which can be further used to predict offset between the initial prediction and the ground-truth as well as output a refined confidence score. Experimental results on four public datasets including MSRA-TD500, ICDAR2017-RCTW, ICDAR2015, and COCO-Text demonstrate that FC2RN can outperform the state-of-the-art methods. The ablation study shows the effectiveness of corner refinement and scoring for accurate text localization.	['Yinliang Yue', 'Xugong Qin', 'Yu Zhou', 'Dayan Wu', 'Weiping Wang']	2020-07-10	null	null	null	null	['multi-oriented-scene-text-detection', 'scene-text-detection']	['computer-vision', 'computer-vision']	[-1.55119091e-01 -5.22194922e-01 1.92929551e-01 -5.29605616e-03 -8.77147257e-01 -4.05623525e-01 5.09344399e-01 3.72027010e-01 -3.95687819e-01 -5.80839477e-02 2.29964301e-01 -8.55166689e-02 3.54398370e-01 -6.85416877e-01 -5.20045638e-01 -6.81991339e-01 5.20869792e-01 6.26480877e-01 8.18209529e-01 -1.43853575e-01 5.85538924e-01 2.88170546e-01 -1.16617131e+00 4.67971295e-01 7.55970776e-01 9.62486446e-01 4.95122910e-01 7.18499720e-01 -2.35372692e-01 4.71450269e-01 -5.65626144e-01 -3.85421723e-01 -2.01814286e-02 -3.00377561e-03 -1.85799882e-01 2.37901211e-02 4.35285658e-01 -4.46326196e-01 -4.04747814e-01 8.68785918e-01 7.46245325e-01 -2.94686854e-01 8.23051631e-01 -9.11226869e-01 -3.41853231e-01 6.53764665e-01 -1.12899017e+00 4.99919318e-02 3.54475826e-01 -3.82975899e-02 1.16190827e+00 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920aa896-3e12-4f2a-a770-bf2bcf7d9479	is-artificial-data-useful-for-biomedical	1907.01055	null	https://arxiv.org/abs/1907.01055v2	https://arxiv.org/pdf/1907.01055v2.pdf	Is artificial data useful for biomedical Natural Language Processing algorithms?	A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.	['Lucia Specia', 'Julia Ive', 'Zixu Wang', 'Sumithra Velupillai']	2019-07-01	is-artificial-data-useful-for-biomedical-1	https://aclanthology.org/W19-5026	https://aclanthology.org/W19-5026.pdf	ws-2019-8	['temporal-relation-extraction']	['natural-language-processing']	[ 7.50730276e-01 7.91948974e-01 -1.57058388e-01 -4.95978236e-01 -8.90634120e-01 -2.94872165e-01 5.25361598e-01 8.94607008e-01 -7.81940222e-01 1.23683178e+00 3.96526128e-01 -6.07472539e-01 -2.27241874e-01 -6.92853987e-01 -3.98002356e-01 -4.82444048e-01 -1.58828378e-01 8.64729106e-01 -1.87498495e-01 -1.35539681e-01 1.12014217e-02 4.86759514e-01 -1.08160853e+00 8.23153198e-01 8.00471187e-01 5.00788689e-01 2.44615581e-02 7.19424486e-01 -3.06786209e-01 6.30361557e-01 -8.82961512e-01 -6.85575455e-02 8.20902511e-02 -5.00298083e-01 -9.38674033e-01 -2.67333627e-01 -3.53704959e-01 3.97086740e-02 1.33382753e-01 6.17780685e-01 8.72956574e-01 -2.01254919e-01 5.78172684e-01 -1.05797625e+00 -1.21594191e-01 9.44516778e-01 7.49872327e-02 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cdd5c42e-a538-4e2e-b65e-7c3dae3636a3	zero-shot-aspect-based-scientific-document-1	null	null	https://aclanthology.org/2022.bionlp-1.5	https://aclanthology.org/2022.bionlp-1.5.pdf	Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training	We study the zero-shot setting for the aspect-based scientific document summarization task. Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience. However, existing large-scale datasets contain a limited variety of aspects, causing summarization models to over-fit to a small set of aspects and a specific domain. We establish baseline results in zero-shot performance (over unseen aspects and the presence of domain shift), paraphrasing, leave-one-out, and limited supervised samples experimental setups. We propose a self-supervised pre-training approach to enhance the zero-shot performance. We leverage the PubMed structured abstracts to create a biomedical aspect-based summarization dataset. Experimental results on the PubMed and FacetSum aspect-based datasets show promising performance when the model is pre-trained using unlabelled in-domain data.	['Salah Ait Mokhtar', 'Benoit Favre', 'Vassilina Nikoulina', 'Amir Soleimani']	null	null	null	null	bionlp-acl-2022-5	['scientific-article-summarization']	['natural-language-processing']	[ 5.41358650e-01 5.31193078e-01 -7.02492893e-01 -3.49916816e-01 -1.48283529e+00 -4.23757672e-01 6.08061671e-01 7.48599112e-01 -3.31686497e-01 1.04064727e+00 9.53733623e-01 1.37723060e-02 -1.24912836e-01 -4.56855863e-01 -7.60057986e-01 -4.33914840e-01 2.94192195e-01 8.08297575e-01 2.31868565e-01 -4.23605777e-02 5.59329093e-01 -5.82229234e-02 -1.27372658e+00 6.09489143e-01 1.28861403e+00 1.99714094e-01 1.78316515e-02 8.91713202e-01 -3.87167245e-01 4.12433922e-01 -1.13695073e+00 -3.27861816e-01 -2.38331139e-01 -7.14578927e-01 -8.78520429e-01 5.14423959e-02 5.25160074e-01 1.12500191e-01 -7.50575587e-02 8.48887205e-01 1.07240367e+00 1.98975086e-01 9.35372293e-01 -7.43113458e-01 -6.27508581e-01 7.37818658e-01 -6.71541750e-01 2.67425388e-01 5.59629023e-01 -5.05847344e-03 1.04746962e+00 -5.18847525e-01 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b052dc3a-41a2-4a5d-951b-14b7c1be358c	manifold-contrastive-learning-with	2306.13544	null	https://arxiv.org/abs/2306.13544v1	https://arxiv.org/pdf/2306.13544v1.pdf	Manifold Contrastive Learning with Variational Lie Group Operators	Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed models of the ventral stream of biological vision, it is observed that these networks lead to a separation of category manifolds in the representations of the penultimate layer. Although this observation matches the manifold hypothesis of representation learning, current self-supervised approaches are limited in their ability to explicitly model this manifold. Indeed, current approaches often only apply augmentations from a pre-specified set of "positive pairs" during learning. In this work, we propose a contrastive learning approach that directly models the latent manifold using Lie group operators parameterized by coefficients with a sparsity-promoting prior. A variational distribution over these coefficients provides a generative model of the manifold, with samples which provide feature augmentations applicable both during contrastive training and downstream tasks. Additionally, learned coefficient distributions provide a quantification of which transformations are most likely at each point on the manifold while preserving identity. We demonstrate benefits in self-supervised benchmarks for image datasets, as well as a downstream semi-supervised task. In the former case, we demonstrate that the proposed methods can effectively apply manifold feature augmentations and improve learning both with and without a projection head. In the latter case, we demonstrate that feature augmentations sampled from learned Lie group operators can improve classification performance when using few labels.	['Christopher J. Rozell', 'Kyle A. Johnsen', 'Alec Helbling', 'Kion Fallah']	2023-06-23	null	null	null	null	['contrastive-learning', 'self-supervised-learning', 'contrastive-learning']	['computer-vision', 'computer-vision', 'methodology']	[ 3.49590242e-01 3.06822240e-01 -4.07642543e-01 -4.09846663e-01 -3.96997094e-01 -6.38848186e-01 1.10175383e+00 2.05106921e-02 -1.88331187e-01 4.21651423e-01 2.70592928e-01 -3.30702849e-02 -1.85240239e-01 -7.05061197e-01 -9.93339181e-01 -8.14972818e-01 -6.83416575e-02 3.26465786e-01 -3.00279349e-01 -2.48300165e-01 1.31923646e-01 5.52046299e-01 -1.55185378e+00 2.88560092e-01 7.53782332e-01 9.11715508e-01 -5.04390709e-02 1.67425379e-01 -2.42481783e-01 4.65049863e-01 -1.09956995e-01 -7.20579773e-02 6.12854302e-01 -4.06442016e-01 -7.89172709e-01 4.67093855e-01 8.48465860e-01 1.92026561e-03 -4.05937195e-01 1.04742384e+00 -1.01086132e-01 2.51828969e-01 1.34197640e+00 -1.40269315e+00 -7.82827914e-01 4.59413230e-01 -5.27373612e-01 -2.19667293e-02 -1.18406020e-01 -7.90962726e-02 1.37543678e+00 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9586c75c-f899-4510-8918-075f9542fb5b	mask-detection-and-classification-in-thermal	2304.02931	null	https://arxiv.org/abs/2304.02931v1	https://arxiv.org/pdf/2304.02931v1.pdf	Mask Detection and Classification in Thermal Face Images	Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the "nano" version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.	['Jacek Rumiński', 'Natalia Kowalczyk']	2023-04-06	null	null	null	null	['type']	['speech']	[ 2.25258067e-01 1.07194647e-01 4.06336397e-01 -2.33675048e-01 -6.97040334e-02 -4.50722098e-01 5.25999308e-01 -1.52527571e-01 -3.76462787e-01 3.52603614e-01 -4.33664352e-01 -7.88794830e-02 2.67363131e-01 -5.87890744e-01 -6.72648847e-01 -1.06422496e+00 5.50321117e-03 5.80971658e-01 9.22465138e-03 -6.09695241e-02 -5.73225878e-02 9.41667378e-01 -1.71379662e+00 6.03519380e-01 2.97051847e-01 1.21386158e+00 6.22361362e-01 3.44070822e-01 2.77975231e-01 4.30779666e-01 -9.91096437e-01 -1.27176225e-01 2.02295855e-01 -2.36463860e-01 -3.69572669e-01 9.06931609e-02 4.87347066e-01 -4.89291519e-01 2.42773533e-01 8.88795555e-01 1.64918870e-01 -4.07238334e-01 8.85768831e-01 -7.48499572e-01 -8.20640251e-02 1.85021907e-01 -2.52689451e-01 3.55701089e-01 2.05797017e-01 8.30440745e-02 1.97173402e-01 -8.71577859e-01 7.77459562e-01 9.39726591e-01 5.50846815e-01 5.90512753e-01 -1.15298605e+00 -6.12338543e-01 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ddddcfab-67aa-464c-8186-635f2429b6f9	decontamination-of-the-scientific-literature	2210.15912	null	https://arxiv.org/abs/2210.15912v1	https://arxiv.org/pdf/2210.15912v1.pdf	Decontamination of the scientific literature	Research misconduct and frauds pollute the scientific literature. Honest errors and malevolent data fabrication, image manipulation, journal hijacking, and plagiarism passed peer review unnoticed. Problematic papers deceive readers, authors citing them, and AI-powered literature-based discovery. Flagship publishers accepted hundreds flawed papers despite claiming to enforce peer review. This application ambitions to decontaminate the scientific literature using curative and preventive actions.	['Guillaume Cabanac']	2022-10-28	null	null	null	null	['image-manipulation']	['computer-vision']	[-7.24442899e-02 1.80908442e-01 -2.21548840e-01 3.09210628e-01 -3.21690559e-01 -1.04786813e+00 7.07068503e-01 1.31979048e-01 -5.64645112e-01 1.27760303e+00 -5.18463366e-02 -1.16520166e+00 1.48191765e-01 -4.13057476e-01 -1.25232375e+00 -2.11324796e-01 5.50761819e-01 -1.16506122e-01 -4.91907805e-01 5.33079743e-01 1.73626864e+00 6.65867627e-01 -7.42805064e-01 -3.87770772e-01 1.03193700e+00 2.83826375e-03 -2.45345205e-01 6.01011753e-01 -1.01974130e-01 1.17799878e+00 -1.32483315e+00 -1.17251694e+00 1.30825862e-01 -2.53143668e-01 -4.45729524e-01 -3.89056504e-01 7.68244922e-01 -3.74661714e-01 -2.16632620e-01 1.57313240e+00 -8.20006803e-02 -5.23443639e-01 1.35213174e-02 -1.32704556e+00 -1.70094037e+00 4.20267910e-01 -9.77999210e-01 5.94832480e-01 3.39083970e-01 4.78392690e-01 3.97511750e-01 -1.07731342e+00 1.17070699e+00 1.32761788e+00 4.58909810e-01 2.93153703e-01 -1.06766343e+00 -1.46101081e+00 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16e8f1f8-3078-4bcf-92a6-b4fcb76bba5f	seamless-multimodal-biometrics-for-continuous	2301.03045	null	https://arxiv.org/abs/2301.03045v2	https://arxiv.org/pdf/2301.03045v2.pdf	Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring	Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)	['João Ribeiro Pinto']	2023-01-08	null	null	null	null	['multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'speech']	[ 2.80756444e-01 1.50686130e-02 1.61903396e-01 -7.39091158e-01 -6.27977431e-01 -4.50683296e-01 1.90084323e-01 -1.84147552e-01 -3.87979418e-01 5.77813923e-01 -1.43445924e-01 -3.26206237e-01 -1.63857266e-01 -5.55117965e-01 -4.20041293e-01 -7.85803080e-01 9.41229388e-02 -1.63444832e-01 -6.86285615e-01 -2.21944004e-01 6.95430189e-02 7.97570705e-01 -1.97025490e+00 1.83145091e-01 6.44178271e-01 1.34799492e+00 -6.18188977e-01 6.45473063e-01 4.27419782e-01 2.58942932e-01 -5.24782002e-01 -9.79427934e-01 2.96519488e-01 -2.50832528e-01 -3.14875752e-01 -2.18291692e-02 5.93284249e-01 -1.39248520e-01 -3.88230532e-01 6.92520320e-01 1.14721525e+00 -2.94908404e-01 4.47485775e-01 -1.64636552e+00 -3.60189557e-01 1.77775566e-02 -4.57853287e-01 -5.34239970e-02 2.49615252e-01 6.09766126e-01 1.72995135e-01 -8.32904756e-01 7.58755282e-02 8.16616058e-01 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60298304-e728-4e3a-af6c-0832039ba3cb	the-winnability-of-klondike-and-many-other	1906.12314	null	https://arxiv.org/abs/1906.12314v4	https://arxiv.org/pdf/1906.12314v4.pdf	The Winnability of Klondike Solitaire and Many Other Patience Games	Our ignorance of the winnability percentage of the game in the Windows Solitaire program, more properly called 'Klondike', has been described as "one of the embarrassments of applied mathematics". Klondike is just one of many single-player card games, generically called 'patience' or 'solitaire' games, for which players have long wanted to know how likely a particular game is to be winnable. A number of different games have been studied empirically in the academic literature and by non-academic enthusiasts. Here we show that a single general purpose Artificial Intelligence program, called "Solvitaire", can be used to determine the winnability percentage of 45 different single-player card games with a 95% confidence interval of +/- 0.1% or better. For example, we report the winnability of Klondike as 81.956% +/- 0.096% (in the 'thoughtful' variant where the player knows the location of all cards), a 30-fold reduction in confidence interval over the best previous result. Almost all our results are either entirely new or represent significant improvements on previous knowledge.	['Ian P. Gent', 'Charlie Blake']	2019-06-28	null	null	null	null	['klondike', 'solitaire', 'card-games']	['playing-games', 'playing-games', 'playing-games']	[-4.53218728e-01 1.38198689e-01 4.85238396e-02 4.82142493e-02 -6.16272449e-01 -8.60978305e-01 1.38082623e-01 4.65025008e-02 -7.53353179e-01 1.10032034e+00 -4.63773429e-01 -9.99266207e-01 -6.50811791e-01 -1.07808888e+00 -7.31322706e-01 -5.50114810e-01 -7.75154978e-02 7.37986088e-01 4.48305815e-01 -4.37313318e-01 8.04113030e-01 2.88466718e-02 -1.30526125e+00 3.58372852e-02 7.29715288e-01 6.48274064e-01 6.69100210e-02 1.05195510e+00 3.05399567e-01 8.43611181e-01 -8.86590302e-01 -8.28817785e-01 1.98728278e-01 -1.60746783e-01 -8.96806657e-01 -6.69666350e-01 -1.68810874e-01 -2.50217885e-01 5.49242124e-02 1.28549695e+00 2.03361839e-01 -2.02419441e-02 4.05786961e-01 -1.43846941e+00 -1.64413363e-01 8.45386386e-01 -7.22656012e-01 5.69816947e-01 4.37915236e-01 7.41998181e-02 9.98268723e-01 -1.64745152e-01 2.31471404e-01 7.79961228e-01 9.01378095e-01 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641beedd-d9d8-410e-bb89-1a6acb6867c7	specificity-preserving-rgb-d-saliency	2108.08162	null	https://arxiv.org/abs/2108.08162v2	https://arxiv.org/pdf/2108.08162v2.pdf	Specificity-preserving RGB-D Saliency Detection	Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing RGB-D SOD models usually adopt different fusion strategies to learn a shared representation from the two modalities (\ie, RGB and depth), while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, termed SPNet} (Specificity-preserving network), which benefits SOD performance by exploring both the shared information and modality-specific properties (\eg, specificity). Specifically, we propose to adopt two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps, respectively. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information. Moreover, to capture rich complementary multi-modal information for boosting the SOD performance, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using a skip connection, the hierarchical features between the encoder and decoder layers can be fully combined. Extensive experiments demonstrate that our~\ours~outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at: https://github.com/taozh2017/SPNet.	['Deng-Ping Fan', 'Yi Zhou', 'Geng Chen', 'Huazhu Fu', 'Tao Zhou']	2021-08-18	null	http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Specificity-Preserving_RGB-D_Saliency_Detection_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Zhou_Specificity-Preserving_RGB-D_Saliency_Detection_ICCV_2021_paper.pdf	iccv-2021-1	['thermal-image-segmentation']	['computer-vision']	[ 3.32252860e-01 -1.88496336e-01 -4.17201698e-01 -4.49395180e-01 -8.19102764e-01 -1.44279182e-01 3.46647173e-01 -2.72356778e-01 -2.13769406e-01 5.73608220e-01 3.48751485e-01 1.56772912e-01 3.06047890e-02 -6.60961926e-01 -7.40911841e-01 -7.67930031e-01 4.55295682e-01 -5.00505328e-01 6.71423316e-01 -2.78894544e-01 7.89792687e-02 2.71292150e-01 -1.81044877e+00 4.51971710e-01 1.05600834e+00 1.46382391e+00 6.73329234e-01 2.62351364e-01 4.47900705e-02 9.25297081e-01 -1.99915487e-02 -3.53766352e-01 2.77103961e-01 -3.87418449e-01 -4.75395709e-01 4.02795114e-02 3.40240180e-01 -5.08609951e-01 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0ad67646-e791-4a41-93a1-049fffcc65d7	adversarial-deep-structured-nets-for-mass	1710.09288	null	http://arxiv.org/abs/1710.09288v2	http://arxiv.org/pdf/1710.09288v2.pdf	Adversarial Deep Structured Nets for Mass Segmentation from Mammograms	Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. \footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}	['Trac. D. Tran', 'Xiang Xiang', 'Wentao Zhu', 'Xiaohui Xie', 'Gregory D. Hager']	2017-10-24	null	null	null	null	['mass-segmentation-from-mammograms']	['medical']	[ 1.95526436e-01 5.76847017e-01 -3.00590754e-01 -7.25187659e-01 -8.97567272e-01 3.78179811e-02 1.90974519e-01 2.37577432e-03 -4.53803927e-01 5.73799491e-01 9.28556100e-02 -5.91162205e-01 3.72856796e-01 -1.02089405e+00 -8.90034616e-01 -7.14092016e-01 -1.49147943e-01 5.40320992e-01 4.85575765e-01 -4.43959236e-02 -3.17321718e-01 1.80393279e-01 -5.90023041e-01 2.47673988e-01 1.00037348e+00 1.22871566e+00 2.04491943e-01 5.89173257e-01 -1.40170708e-01 7.53546178e-01 -1.55309111e-01 -6.72525406e-01 2.57195592e-01 -4.76719081e-01 -9.07193720e-01 4.02204879e-02 2.60128796e-01 -5.83365798e-01 -6.55709803e-01 1.25323999e+00 5.45040190e-01 -1.49883017e-01 6.84051037e-01 -8.53320241e-01 -4.55096811e-01 8.31767023e-01 -8.81512046e-01 3.84772271e-01 -3.45074356e-01 8.56712684e-02 4.89448428e-01 -5.75876176e-01 4.87393290e-01 1.03457260e+00 9.66308773e-01 7.08910048e-01 -8.68831277e-01 -9.36827481e-01 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4d5a2001-1554-452f-b6d9-db57cdb95153	network-giant-fully-distributed-newton-type	2305.07898	null	https://arxiv.org/abs/2305.07898v1	https://arxiv.org/pdf/2305.07898v1.pdf	Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus	This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimization and information exchange between neighbouring nodes. We introduce a Newton-type fully distributed optimization algorithm, Network-GIANT, which is based on GIANT, a Federated learning algorithm that relies on a centralized parameter server. The Network-GIANT algorithm is designed via a combination of gradient-tracking and a Newton-type iterative algorithm at each node with consensus based averaging of local gradient and Newton updates. We prove that our algorithm guarantees semi-global and exponential convergence to the exact solution over the network assuming strongly convex and smooth loss functions. We provide empirical evidence of the superior convergence performance of Network-GIANT over other state-of-art distributed learning algorithms such as Network-DANE and Newton-Raphson Consensus.	['Subhrakanti Dey', 'Luca Schenato', 'Ganesh Sharma', 'Alessio Maritan']	2023-05-13	null	null	null	null	['distributed-optimization', 'type']	['methodology', 'speech']	[-6.49768054e-01 7.45093375e-02 4.01629768e-02 -3.13558549e-01 -1.26629484e+00 -4.35752153e-01 2.52305299e-01 3.42152089e-01 -7.26850927e-01 1.10879922e+00 -5.11660948e-02 9.93471071e-02 -6.44903660e-01 -6.56140387e-01 -8.46599817e-01 -1.14645123e+00 -7.56354332e-01 1.03226793e+00 -7.30784163e-02 -1.04408152e-02 -5.35024935e-03 1.76774561e-01 -7.98745871e-01 -1.24308839e-01 7.57415771e-01 9.82385635e-01 -1.18557096e-01 1.12895751e+00 2.45850265e-01 1.06038117e+00 -3.99385065e-01 -3.85469466e-01 7.44166732e-01 -4.24513191e-01 -7.60073781e-01 -1.03568867e-01 5.96076727e-01 -3.79807293e-01 -5.20683378e-02 1.09439802e+00 8.44244957e-01 3.01855475e-01 3.26894045e-01 -1.45465732e+00 -1.37958393e-01 9.27696943e-01 -5.69220662e-01 -6.72209859e-02 -5.84607460e-02 1.42756343e-01 1.32653546e+00 -6.59521580e-01 6.14369035e-01 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1.13634443e+00 -1.00757873e+00 -1.49554324e+00 -6.05301142e-01 -8.95867348e-01 1.71662301e-01 4.14094031e-01 2.44684413e-01 -9.56833303e-01 -3.19617093e-01]	[6.197488784790039, 5.059884548187256]
f52cfd10-d71a-4ca4-9806-c23b85e678a6	accuracy-of-segment-anything-model-sam-in	2304.09324	null	https://arxiv.org/abs/2304.09324v3	https://arxiv.org/pdf/2304.09324v3.pdf	Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets	Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes without previously-required re-training or fine-tuning specific to each new dataset. Purpose: To test SAM's accuracy in various medical image segmentation tasks and investigate potential factors that may affect its accuracy in medical images. Methods: SAM was tested on 12 public medical image segmentation datasets involving 7,451 subjects. The accuracy was measured by the Dice overlap between the algorithm-segmented and ground-truth masks. SAM was compared with five state-of-the-art algorithms specifically designed for medical image segmentation tasks. Associations of SAM's accuracy with six factors were computed, independently and jointly, including segmentation difficulties as measured by segmentation ability score and by Dice overlap in U-Net, image dimension, size of the target region, image modality, and contrast. Results: The Dice overlaps from SAM were significantly lower than the five medical-image-based algorithms in all 12 medical image segmentation datasets, by a margin of 0.1-0.5 and even 0.6-0.7 Dice. SAM-Semantic was significantly associated with medical image segmentation difficulty and the image modality, and SAM-Point and SAM-Box were significantly associated with image segmentation difficulty, image dimension, target region size, and target-vs-background contrast. All these 3 variations of SAM were more accurate in 2D medical images, larger target region sizes, easier cases with a higher Segmentation Ability score and higher U-Net Dice, and higher foreground-background contrast.	['Yangming Ou', 'Atle Bjornerud', 'Jeffrey Stout', 'P. Ellen Grant', 'Jingpeng Li', 'Rina Bao', 'Sheng He']	2023-04-18	null	null	null	null	['zero-shot-segmentation']	['computer-vision']	[ 4.02083665e-01 1.80073544e-01 -3.21221977e-01 -4.96701062e-01 -9.43898022e-01 -7.01488614e-01 2.09884942e-01 2.76116759e-01 -7.98348963e-01 3.59824687e-01 -1.64066404e-02 -3.55605841e-01 -1.83752745e-01 -5.21453202e-01 -3.17326546e-01 -5.76983154e-01 -2.32289173e-02 5.56823075e-01 4.30941075e-01 2.12745532e-01 2.80189842e-01 4.39766884e-01 -8.41559887e-01 1.26693264e-01 1.17170012e+00 9.66374874e-01 4.27550346e-01 7.63964117e-01 7.94588253e-02 2.20532998e-01 -5.96996188e-01 -2.24154025e-01 3.76102507e-01 -6.85119033e-01 -9.00765777e-01 4.13830489e-01 6.29492760e-01 -2.10302770e-01 4.05232310e-02 9.17484343e-01 7.09554732e-01 -6.26146793e-02 9.96452332e-01 -8.02311718e-01 -6.66549802e-01 4.44493383e-01 -8.08682919e-01 5.41693032e-01 -2.46787607e-03 5.76605618e-01 4.04279411e-01 -1.74578220e-01 8.35885108e-01 9.06815112e-01 9.45761144e-01 4.64839339e-01 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6554f4b7-52fb-4f02-b731-b035708005e0	specular-to-diffuse-translation-for-multi	1807.05439	null	http://arxiv.org/abs/1807.05439v3	http://arxiv.org/pdf/1807.05439v3.pdf	Specular-to-Diffuse Translation for Multi-View Reconstruction	Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively. Our network extends unsupervised image-to-image translation to multi-view "specular to diffuse" translation. To preserve object appearance across multiple views, we introduce a Multi-View Coherence loss (MVC) that evaluates the similarity and faithfulness of local patches after the view-transformation. Our MVC loss ensures that the similarity of local correspondences among multi-view images is preserved under the image-to-image translation. As a result, our network yields significantly better results than several single-view baseline techniques. In addition, we carefully design and generate a large synthetic training data set using physically-based rendering. During testing, our network takes only the raw glossy images as input, without extra information such as segmentation masks or lighting estimation. Results demonstrate that multi-view reconstruction can be significantly improved using the images filtered by our network. We also show promising performance on real world training and testing data.	['Danny Cohen-Or', 'Shihao Wu', 'Hui Huang', 'Matthias Zwicker', 'Matan Sela', 'Tiziano Portenier', 'Ron Kimmel']	2018-07-14	specular-to-diffuse-translation-for-multi-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.pdf	eccv-2018-9	['lighting-estimation']	['computer-vision']	[ 3.51193547e-01 -1.96885273e-01 3.17184448e-01 -5.28206110e-01 -7.25874126e-01 -7.95145035e-01 5.65639615e-01 -9.20549810e-01 1.14945605e-01 4.83470201e-01 1.24019325e-01 3.48623618e-02 4.16081697e-01 -9.95517135e-01 -9.91055548e-01 -8.97656798e-01 6.80575252e-01 2.40507200e-01 2.00976923e-01 -2.06908360e-01 3.15024331e-03 6.45752728e-01 -1.36218750e+00 5.77104986e-01 6.21168435e-01 6.76734209e-01 1.72954008e-01 6.44552350e-01 2.20610157e-01 4.83671874e-01 -4.55213428e-01 -4.85173434e-01 7.24200606e-01 -4.88674343e-01 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1f239abe-2a8d-4155-be54-31c5096b09f9	self-attention-presents-low-dimensional	2112.10644	null	https://arxiv.org/abs/2112.10644v3	https://arxiv.org/pdf/2112.10644v3.pdf	Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction	A few models have tried to tackle the link prediction problem, also known as knowledge graph completion, by embedding knowledge graphs in comparably lower dimensions. However, the state-of-the-art results are attained at the cost of considerably increasing the dimensionality of embeddings which causes scalability issues in the case of huge knowledge bases. Transformers have been successfully used recently as powerful encoders for knowledge graphs, but available models still have scalability issues. To address this limitation, we introduce a Transformer-based model to gain expressive low-dimensional embeddings. We utilize a large number of self-attention heads as the key to applying query-dependent projections to capture mutual information between entities and relations. Empirical results on WN18RR and FB15k-237 as standard link prediction benchmarks demonstrate that our model has favorably comparable performance with the current state-of-the-art models. Notably, we yield our promising results with a significant reduction of 66.9% in the dimensionality of embeddings compared to the five best recent state-of-the-art competitors on average.	['Hadi Moradi', 'Reshad Hosseini', 'Peyman Baghershahi']	2021-12-20	null	null	null	null	['knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'methodology']	[-3.01489770e-01 5.30638158e-01 -7.11026609e-01 -3.18507105e-02 -4.78446960e-01 -3.33594650e-01 6.37933969e-01 1.87656686e-01 -3.89792889e-01 6.64719701e-01 4.96184438e-01 -4.20347214e-01 -5.49592376e-01 -1.03602612e+00 -7.61099160e-01 -2.05059722e-01 -3.79684389e-01 8.85934353e-01 2.15257540e-01 -3.54439646e-01 -3.99816930e-01 1.23481564e-01 -1.27926576e+00 5.01297303e-02 7.91105390e-01 7.28697240e-01 -2.97237962e-01 3.45613092e-01 -2.15420485e-01 7.99634516e-01 -1.23361982e-02 -1.15127015e+00 7.88384527e-02 1.86878398e-01 -1.14039624e+00 -5.06011307e-01 3.95555466e-01 -3.35588574e-01 -1.16646218e+00 8.40380013e-01 4.83430713e-01 3.51290032e-02 5.02456367e-01 -1.32969546e+00 -1.40384674e+00 9.52149451e-01 -2.96983510e-01 3.03496748e-01 3.53178144e-01 -5.09387434e-01 1.73729086e+00 -1.01565540e+00 8.63962054e-01 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88522dcb-d1a9-4acd-af6d-0d44c9b77dc4	faceqgen-semi-supervised-deep-learning-for	2201.00770	null	https://arxiv.org/abs/2201.00770v1	https://arxiv.org/pdf/2201.00770v1.pdf	FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment	In this paper we develop FaceQgen, a No-Reference Quality Assessment approach for face images based on a Generative Adversarial Network that generates a scalar quality measure related with the face recognition accuracy. FaceQgen does not require labelled quality measures for training. It is trained from scratch using the SCface database. FaceQgen applies image restoration to a face image of unknown quality, transforming it into a canonical high quality image, i.e., frontal pose, homogeneous background, etc. The quality estimation is built as the similarity between the original and the restored images, since low quality images experience bigger changes due to restoration. We compare three different numerical quality measures: a) the MSE between the original and the restored images, b) their SSIM, and c) the output score of the Discriminator of the GAN. The results demonstrate that FaceQgen's quality measures are good estimators of face recognition accuracy. Our experiments include a comparison with other quality assessment methods designed for faces and for general images, in order to position FaceQgen in the state of the art. This comparison shows that, even though FaceQgen does not surpass the best existing face quality assessment methods in terms of face recognition accuracy prediction, it achieves good enough results to demonstrate the potential of semi-supervised learning approaches for quality estimation (in particular, data-driven learning based on a single high quality image per subject), having the capacity to improve its performance in the future with adequate refinement of the model and the significant advantage over competing methods of not needing quality labels for its development. This makes FaceQgen flexible and scalable without expensive data curation.	['Aythami Morales', 'Ignacio Serna', 'Julian Fierrez', 'Javier Hernandez-Ortega']	2022-01-03	null	null	null	null	['face-image-quality', 'face-image-quality-assessment']	['computer-vision', 'computer-vision']	[ 2.38767475e-01 1.69490799e-01 1.00730553e-01 -5.37360787e-01 -9.06226695e-01 -3.49889308e-01 6.23157442e-01 -4.17628169e-01 -6.11243304e-04 7.07223177e-01 1.93941407e-02 1.43607616e-01 -3.60745639e-01 -9.10378456e-01 -6.22950971e-01 -9.84183073e-01 -4.28344160e-02 4.93728071e-01 -3.09054494e-01 -1.70593739e-01 8.53654221e-02 7.83577800e-01 -1.82355452e+00 1.70317888e-01 7.40795493e-01 1.26870751e+00 -3.18895727e-01 4.76130515e-01 3.12411666e-01 5.46075940e-01 -9.06851113e-01 -8.80919397e-01 4.36425418e-01 -7.29739487e-01 -7.82310069e-01 1.75864339e-01 8.39124322e-01 -3.74998629e-01 -5.88038713e-02 1.06352842e+00 6.78953290e-01 -3.61343116e-01 7.22443938e-01 -1.52615881e+00 -5.44689834e-01 2.93319169e-02 -7.75077417e-02 1.16567880e-01 5.78166127e-01 4.80001569e-01 6.69360936e-01 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1.03146493e+00 -8.45038176e-01 -3.90823007e-01 -9.84935224e-01 -6.44247949e-01 7.11657584e-01 2.22075522e-01 -8.30207467e-01 -3.31650376e-01 1.24180011e-01]	[13.008182525634766, 0.7440258264541626]
84f91aae-a478-4215-9f76-df1287f13929	trailers12k-evaluating-transfer-learning-for	2210.07983	null	https://arxiv.org/abs/2210.07983v4	https://arxiv.org/pdf/2210.07983v4.pdf	Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre Classification	In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we present an extensive evaluation of the transferability of ConvNet and Transformer models pretrained on ImageNet and Kinetics to Trailers12k, a new manually-curated movie trailer dataset composed of 12,000 videos labeled with 10 different genres and associated metadata. We analyze different aspects that can influence transferability, such as frame rate, input video extension, and spatio-temporal modeling. In order to reduce the spatio-temporal structure gap between ImageNet/Kinetics and Trailers12k, we propose Dual Image and Video Transformer Architecture (DIViTA), which performs shot detection so as to segment the trailer into highly correlated clips, providing a more cohesive input for pretrained backbones and improving transferability (a 1.83% increase for ImageNet and 3.75% for Kinetics). Our results demonstrate that representations learned on either ImageNet or Kinetics are comparatively transferable to Trailers12k. Moreover, both datasets provide complementary information that can be combined to improve classification performance (a 2.91% gain compared to the top single pretraining). Interestingly, using lightweight ConvNets as pretrained backbones resulted in only a 3.46% drop in classification performance compared with the top Transformer while requiring only 11.82% of its parameters and 0.81% of its FLOPS.	['Gibran Fuentes-Pineda', 'Berenice Montalvo-Lezama', 'Ricardo Montalvo-Lezama']	2022-10-14	null	null	null	null	['genre-classification']	['computer-vision']	[ 6.48892671e-02 -4.67199266e-01 -1.93432391e-01 -1.58966273e-01 -6.13852680e-01 -8.78432751e-01 4.75735754e-01 -1.56789497e-02 -7.65135229e-01 3.92623484e-01 1.00463055e-01 -4.28279154e-02 -2.50954945e-02 -5.61911523e-01 -9.75590765e-01 -6.32591903e-01 -2.51844496e-01 7.12160394e-02 4.86657351e-01 -2.55387396e-01 -9.39614475e-02 3.15057129e-01 -1.57830763e+00 1.08330190e+00 4.16938782e-01 1.68654323e+00 6.54945821e-02 7.98475981e-01 3.48665923e-01 1.26438355e+00 -5.80292404e-01 -3.95349681e-01 2.67611623e-01 -1.73081204e-01 -7.53865957e-01 6.98154047e-02 1.02046895e+00 -4.91075337e-01 -7.56394565e-01 3.91276777e-01 2.45103136e-01 3.17292720e-01 8.07301521e-01 -1.39136732e+00 -6.53933108e-01 5.99153996e-01 -4.27553535e-01 5.82490981e-01 1.15656666e-01 2.35144615e-01 9.40085709e-01 -8.03183615e-01 9.16946411e-01 1.04366720e+00 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-6.88375980e-02 -8.61700177e-01 -5.28490603e-01 7.12356389e-01 1.01649232e-01 -7.88062811e-01 -2.46056795e-01 5.38211167e-01]	[9.299049377441406, 0.784055769443512]
4a740db7-dbe7-4b6f-8620-46df0d6fbb9b	video-object-segmentation-using-supervoxel	1704.05165	null	http://arxiv.org/abs/1704.05165v1	http://arxiv.org/pdf/1704.05165v1.pdf	Video Object Segmentation using Supervoxel-Based Gerrymandering	Pixels operate locally. Superpixels have some potential to collect information across many pixels; supervoxels have more potential by implicitly operating across time. In this paper, we explore this well established notion thoroughly analyzing how supervoxels can be used in place of and in conjunction with other means of aggregating information across space-time. Focusing on the problem of strictly unsupervised video object segmentation, we devise a method called supervoxel gerrymandering that links masks of foregroundness and backgroundness via local and non-local consensus measures. We pose and answer a series of critical questions about the ability of supervoxels to adequately sway local voting; the questions regard type and scale of supervoxels as well as local versus non-local consensus, and the questions are posed in a general way so as to impact the broader knowledge of the use of supervoxels in video understanding. We work with the DAVIS dataset and find that our analysis yields an unsupervised method that outperforms all other known unsupervised methods and even many supervised ones.	['Jason J. Corso', 'Brent A. Griffin']	2017-04-18	null	null	null	null	['unsupervised-video-object-segmentation']	['computer-vision']	[ 5.57291508e-01 -7.29857385e-02 -2.83805102e-01 -4.13366616e-01 -5.52722991e-01 -9.70958471e-01 8.99530053e-01 2.63072371e-01 -4.93206203e-01 3.63993287e-01 2.17799678e-01 -3.57773483e-01 -3.60638618e-01 -4.66080725e-01 -5.16088843e-01 -9.29693580e-01 -2.57417355e-02 2.10944206e-01 8.13889623e-01 -3.21293138e-02 4.46011573e-01 2.78231651e-01 -1.52360463e+00 3.27230513e-01 9.01210189e-01 6.96431994e-01 -7.31036067e-04 8.65281641e-01 9.21479464e-02 1.10696805e+00 -6.01352036e-01 -1.61902636e-01 3.29434216e-01 -4.51541603e-01 -7.78898180e-01 6.06869102e-01 9.86684680e-01 -1.37606129e-01 6.02544993e-02 1.16170621e+00 3.43665704e-02 1.54445276e-01 6.39705896e-01 -1.10488033e+00 -3.38318169e-01 5.93648195e-01 -6.07280314e-01 7.23533332e-01 6.84710294e-02 2.30232030e-01 1.16550457e+00 -5.04989684e-01 7.72819579e-01 1.15411937e+00 7.06286788e-01 2.41549343e-01 -1.24399674e+00 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1e709c36-68e9-449f-8597-82e675a4d5e7	deep-multi-branch-cnn-architecture-for-early	2210.12331	null	https://arxiv.org/abs/2210.12331v3	https://arxiv.org/pdf/2210.12331v3.pdf	Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs	Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD induced dementia and unpaid care for people with AD related dementia is valued at $271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for early detection of AD. We then give an overview of our dataset that was from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and propose a deep Convolutional Neural Network (CNN) architecture consisting of 7,866,819 parameters. This model has three different convolutional branches with each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three class accuracy.	['Rakesh Mahto', 'Paul K. Mandal']	2022-10-22	null	null	null	null	['alzheimer-s-disease-detection']	['medical']	[-6.40599579e-02 4.71615121e-02 1.13227956e-01 -6.71157837e-01 -3.62659991e-01 -1.76509336e-01 2.83283025e-01 8.08912795e-03 -9.05505896e-01 1.23977208e+00 3.41085672e-01 -4.96822655e-01 1.13860153e-01 -8.16018283e-01 -3.48815918e-01 -2.78478891e-01 -5.06953835e-01 6.35334194e-01 4.70020533e-01 1.22263260e-01 -2.16065407e-01 5.13824284e-01 -9.64915037e-01 4.78680551e-01 1.15866661e+00 1.10842001e+00 3.78179580e-01 7.71733701e-01 1.20786846e-01 6.47140682e-01 -6.60327554e-01 -2.13419929e-01 2.21058249e-01 -1.30617231e-01 -8.26328516e-01 -2.15659007e-01 4.38553631e-01 -1.23175395e+00 -3.72027129e-01 9.67577517e-01 7.06202269e-01 -4.83935148e-01 8.22718441e-01 -9.38520849e-01 -6.87432170e-01 3.24625410e-02 -1.91672981e-01 8.91444087e-01 -2.35963181e-01 4.41466607e-02 4.10175771e-01 -6.65874720e-01 1.87316671e-01 1.39575446e+00 8.04563642e-01 7.80218840e-01 -9.01088059e-01 -6.51424885e-01 7.49368370e-02 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f7b81179-843d-493a-b0ce-945422323d01	playing-atari-games-with-deep-reinforcement	1607.05077	null	http://arxiv.org/abs/1607.05077v1	http://arxiv.org/pdf/1607.05077v1.pdf	Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay	This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as epsilon-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma's Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.	['Ionel-Alexandru Hosu', 'Traian Rebedea']	2016-07-18	null	null	null	null	['montezumas-revenge']	['playing-games']	[-3.47729176e-01 1.97766498e-01 -2.90552080e-02 2.07262591e-01 -7.79217184e-01 -5.62832773e-01 7.59357035e-01 -2.17055887e-01 -9.68101740e-01 9.95907605e-01 -1.53056279e-01 -3.67897660e-01 9.40841213e-02 -8.38755369e-01 -7.97934175e-01 -7.84823239e-01 -4.00718361e-01 6.55598342e-01 3.29513550e-01 -5.92684031e-01 3.64777088e-01 1.28100961e-01 -1.41355646e+00 7.41862655e-02 5.62165201e-01 7.66415477e-01 2.43368804e-01 1.07266605e+00 3.23510945e-01 1.64336383e+00 -9.33121800e-01 4.10327725e-02 4.11468297e-01 -4.92159545e-01 -9.78259206e-01 5.39373606e-02 -3.28583330e-01 -8.01454365e-01 -5.61880946e-01 8.54588687e-01 5.27192354e-01 4.26289111e-01 1.93703294e-01 -1.19805777e+00 9.20495689e-02 6.99599445e-01 -5.53231299e-01 2.10335881e-01 3.47889155e-01 7.65942395e-01 7.68315256e-01 -8.00831467e-02 6.75116301e-01 9.67879474e-01 4.26798642e-01 7.26843297e-01 -7.62411714e-01 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1a55376f-3a7a-4d13-85bc-f9b3fc04aebc	augmented-dual-contrastive-aggregation	null	null	https://dl.acm.org/doi/abs/10.1145/3503161.3548198	https://dl.acm.org/doi/abs/10.1145/3503161.3548198	Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification	Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding infrared (visible) images from a gallery set captured by other spectrum cameras. Recent works mainly focus on supervised VI-ReID methods that require plenty of cross-modality (visible-infrared) identity labels which are more expensive than the annotations in single-modality person ReID. For the unsupervised learning visible infrared re-identification (USL-VI-ReID), the large cross-modality discrepancies lead to difficulties in generating reliable cross-modality labels and learning modality-invariant features without any annotations. To address this problem, we propose a novel Augmented Dual-Contrastive Aggregation (ADCA) learning framework. Specifically, a dual-path contrastive learning framework with two modality-specific memories is proposed to learn the intra-modality person representation. To associate positive cross-modality identities, we design a cross-modality memory aggregation module with count priority to select highly associated positive samples, and aggregate their corresponding memory features at the cluster level, ensuring that the optimization is explicitly concentrated on the modality-irrelevant perspective. Extensive experiments demonstrate that our proposed ADCA significantly outperforms existing unsupervised methods under various settings, and even surpasses some supervised counterparts, facilitating VI-ReID to real-world deployment.	['Zesen Wu', 'Jun Chen', 'Mang Ye', 'Bin Yang']	2022-10-14	null	null	null	acm-mm-2022-10	['person-re-identification']	['computer-vision']	[ 3.72130990e-01 -5.98258376e-01 -2.45982826e-01 -3.47369254e-01 -9.80098009e-01 -6.30517125e-01 6.85912967e-01 -9.44703445e-03 -5.36742210e-01 4.46804106e-01 1.82583988e-01 1.92725420e-01 -3.29835534e-01 -5.31964540e-01 -4.65665698e-01 -9.60037231e-01 1.80484384e-01 4.07467186e-01 -5.00361681e-01 9.83461961e-02 1.11633815e-01 3.65131587e-01 -1.84912121e+00 2.08244205e-01 7.40718722e-01 8.43560755e-01 -9.74865854e-02 2.97726572e-01 -4.03264128e-02 6.42902493e-01 -4.42879468e-01 -5.74499488e-01 4.61437136e-01 -2.72725463e-01 -7.09479511e-01 1.76754668e-02 9.50292289e-01 -3.99689637e-02 -2.34373450e-01 1.14928424e+00 6.56466484e-01 5.02565503e-01 7.19106793e-01 -1.49222469e+00 -1.07237840e+00 2.61230975e-01 -1.04032278e+00 2.36049443e-01 6.92765951e-01 -1.71055235e-02 6.70586586e-01 -9.40935671e-01 2.81823874e-01 1.40476191e+00 8.27769399e-01 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207f9989-0ce3-41fd-b5bd-dfb0c1c62e7d	fast-passage-re-ranking-with-contextualized	2108.08513	null	https://arxiv.org/abs/2108.08513v2	https://arxiv.org/pdf/2108.08513v2.pdf	Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion	BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical especially under resource constraints. The reliance on a query encoder that only performs tokenization and on the pre-processing of passage representations at indexing, has allowed the recently proposed TILDE method to overcome the high query latency issue typical of BERT-based models. This however is at the expense of a lower effectiveness compared to other BERT-based re-rankers and dense retrievers. In addition, the original TILDE method is characterised by indexes with a very high memory footprint, as it expands each passage into the size of the BERT vocabulary. In this paper, we propose TILDEv2, a new model that stems from the original TILDE but that addresses its limitations. TILDEv2 relies on contextualized exact term matching with expanded passages. This requires to only store in the index the score of tokens that appear in the expanded passages (rather than all the vocabulary), thus producing indexes that are 99% smaller than those of TILDE. This matching mechanism also improves ranking effectiveness by 24%, without adding to the query latency. This makes TILDEv2 the state-of-the-art passage re-ranking method for CPU-only environments, capable of maintaining query latency below 100ms on commodity hardware.	['Guido Zuccon', 'Shengyao Zhuang']	2021-08-19	null	null	null	null	['passage-re-ranking']	['natural-language-processing']	[ 2.36783903e-02 -4.03998971e-01 -3.63102406e-01 1.93947032e-01 -1.22969997e+00 -4.96582747e-01 6.26976848e-01 8.49479079e-01 -1.01010633e+00 4.40886080e-01 1.21500745e-01 -3.15477401e-01 -4.30648774e-01 -1.13175678e+00 -4.36609834e-01 -1.03378467e-01 -1.94404215e-01 9.09096479e-01 9.79329050e-01 -4.43063587e-01 5.96044123e-01 5.94973922e-01 -1.98729050e+00 3.12050968e-01 5.34783185e-01 1.00282931e+00 3.08411807e-01 7.80353904e-01 -5.62602341e-01 6.58103228e-01 -8.75169218e-01 -7.92987198e-02 2.35790178e-01 2.77157798e-02 -9.59225833e-01 -9.87102270e-01 4.34667587e-01 -7.10900545e-01 -5.56938052e-01 3.29742730e-01 7.05867171e-01 2.32372209e-01 2.88811266e-01 -8.01380038e-01 -3.55413437e-01 5.84516525e-01 -4.17261094e-01 4.29063231e-01 7.70198226e-01 -6.44130647e-01 1.14579892e+00 -8.77056777e-01 7.00328708e-01 8.48131120e-01 5.20320535e-01 2.29279265e-01 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1.19373947e-01 1.60742715e-01 -4.91239130e-01 -6.35640621e-02 -6.47248626e-01]	[11.442876815795898, 7.565459728240967]
6f5c2264-2323-48ed-bf11-15f8a17774af	unsupervised-few-shot-learning-via-deep	2210.03595	null	https://arxiv.org/abs/2210.03595v1	https://arxiv.org/pdf/2210.03595v1.pdf	Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps	Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or meta-learning on labeled meta-training data and cannot be applied to the case where the pretraining data is unlabeled. In this study, we present an unsupervised few-shot learning method via deep Laplacian eigenmaps. Our method learns representation from unlabeled data by grouping similar samples together and can be intuitively interpreted by random walks on augmented training data. We analytically show how deep Laplacian eigenmaps avoid collapsed representation in unsupervised learning without explicit comparison between positive and negative samples. The proposed method significantly closes the performance gap between supervised and unsupervised few-shot learning. Our method also achieves comparable performance to current state-of-the-art self-supervised learning methods under linear evaluation protocol.	['Chi-Guhn Lee', 'Kuilin Chen']	2022-10-07	null	null	null	null	['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification']	['computer-vision', 'computer-vision']	[ 3.90896618e-01 3.88995647e-01 -4.87502754e-01 -6.16139591e-01 -8.38130891e-01 -1.52584314e-01 7.03617513e-01 2.78847992e-01 -4.44661885e-01 6.47486210e-01 2.73173273e-01 1.74357250e-01 -1.27480403e-01 -9.15368080e-01 -4.80907738e-01 -5.88626802e-01 -1.15141543e-02 8.24002087e-01 3.82159173e-01 -1.31327271e-01 2.73433439e-02 -1.74810648e-01 -1.68687880e+00 2.88848788e-01 6.53529942e-01 6.83128893e-01 4.68257032e-02 5.60655415e-01 -4.85353410e-01 7.50253975e-01 -1.70739815e-01 -1.81624189e-01 2.35896006e-01 -6.37786269e-01 -8.78437102e-01 2.87899792e-01 3.46630663e-01 -1.12402335e-01 -2.50563741e-01 1.04670799e+00 6.42757058e-01 6.86307311e-01 1.09420180e+00 -1.21256804e+00 -7.65650630e-01 5.21485269e-01 -6.30453408e-01 2.15555623e-01 -1.16796233e-01 1.35507032e-01 1.14268649e+00 -1.20105028e+00 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7574ec54-f3a3-412b-9b32-06832db8109f	state-representation-learning-using-an	2305.10267	null	https://arxiv.org/abs/2305.10267v1	https://arxiv.org/pdf/2305.10267v1.pdf	State Representation Learning Using an Unbalanced Atlas	The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations. While numerous traditional manifold-based techniques exist for dimensionality reduction, their application in self-supervised learning has witnessed slow progress. The recent MSIMCLR method combines manifold encoding with SimCLR but requires extremely low target encoding dimensions to outperform SimCLR, limiting its applicability. This paper introduces a novel learning paradigm using an unbalanced atlas (UA), capable of surpassing state-of-the-art self-supervised learning approaches. We meticulously investigated and engineered the DeepInfomax with an unbalanced atlas (DIM-UA) method by systematically adapting the Spatiotemporal DeepInfomax (ST-DIM) framework to align with our proposed UA paradigm, employing rigorous scientific methodologies throughout the process. The efficacy of DIM-UA is demonstrated through training and evaluation on the Atari Annotated RAM Interface (AtariARI) benchmark, a modified version of the Atari 2600 framework that produces annotated image samples for representation learning. The UA paradigm improves the existing algorithm significantly when the number of target encoding dimensions grows. For instance, the mean F1 score averaged over categories of DIM-UA is ~75% compared to ~70% of ST-DIM when using 16384 hidden units.	['Paal Engelstad', 'Anis Yazidi', 'Morten Goodwin', 'Li Meng']	2023-05-17	null	null	null	null	['dimensionality-reduction']	['methodology']	[-4.94420109e-03 8.29975381e-02 -1.87919959e-01 -1.49042696e-01 -7.77123213e-01 -4.36349660e-01 7.28648007e-01 -1.26330450e-01 -3.22896808e-01 4.52890009e-01 2.06494898e-01 -2.05717534e-01 -4.09069479e-01 -6.34583175e-01 -6.83401346e-01 -6.64312065e-01 -5.34512877e-01 5.14438093e-01 -3.06686223e-01 -2.69729674e-01 2.46737272e-01 6.45933747e-01 -2.11012816e+00 6.99860081e-02 6.54418588e-01 1.09273136e+00 6.65933788e-02 5.00444412e-01 -1.68323532e-01 6.90968513e-01 -7.24553525e-01 6.75158156e-03 4.20940250e-01 -3.72156769e-01 -8.41939628e-01 -7.41703138e-02 8.37924600e-01 -2.11908482e-02 -6.52433395e-01 9.03055847e-01 4.62708652e-01 4.56682205e-01 8.43607187e-01 -1.06335974e+00 -9.31399167e-01 3.95555824e-01 -5.02052188e-01 4.56217229e-01 -2.05085844e-01 -4.48634773e-02 9.50708032e-01 -7.96475232e-01 7.01383233e-01 1.18047333e+00 4.67050612e-01 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9af32409-b39c-4a78-88ce-3d204e4b850f	experiencing-the-communication-advantage-of	null	null	https://ieeexplore.ieee.org/document/9593171	https://spawc2021.myquadra.it/wp-content/paper/1570721633-1.pdf	Experiencing the communication advantage of the Indefinite Causal Orders	Many recent studies deal with the Superposition of Causal Orders, a quantum operation with promising advantages in both communication or computing. To experience the advantages, there are several way of implementing it. In literature, most of the set-ups are photonic-based. Instead, our interest is witnessing the Superpositon of Causal Orders within a programmable technology, based on superconductors. To do that, we focus on a specific case of the subject operation, which could be useful for the future of quantum communication.	['Daniele Cuomo; Marcello Caleffi; Angela Sara Cacciapuoti']	2021-11-19	null	null	null	ieee-spawc-2021-11	['noise-estimation']	['medical']	[ 4.01327103e-01 -2.33438984e-02 -1.44686148e-01 -1.13860182e-01 3.98069769e-01 -4.39359874e-01 6.22871935e-01 -6.61343694e-01 6.42608404e-02 9.91513908e-01 3.01508307e-02 -2.60636091e-01 -3.13887149e-01 -1.19052446e+00 -4.22858119e-01 -9.67692852e-01 -3.14137995e-01 -9.69337150e-02 6.27014399e-01 -5.05627751e-01 8.81486297e-01 2.79479176e-01 -1.62901592e+00 3.41307551e-01 7.54167438e-01 1.11319661e+00 2.26348490e-02 2.96740383e-01 5.19490838e-02 4.38031018e-01 -4.50361341e-01 -2.09922105e-01 2.71164924e-01 -8.68592799e-01 -6.15542710e-01 -5.81710815e-01 -1.79282919e-01 -2.13185370e-01 -7.92116344e-01 1.42030263e+00 3.97570096e-02 -3.36568147e-01 3.24189574e-01 -1.07789803e+00 -6.99423790e-01 9.42035913e-01 7.81403333e-02 6.69830292e-02 4.21064407e-01 2.64701039e-01 9.29937363e-01 -8.85086805e-02 6.69988036e-01 7.64440715e-01 -9.91952419e-02 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50e465a8-fc4e-406b-867c-c78dc9745b97	a-gaussian-scale-space-approach-for-exudates	1505.00737	null	http://arxiv.org/abs/1505.00737v1	http://arxiv.org/pdf/1505.00737v1.pdf	A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction	In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54% and prediction of 98.35% in DIARETDB1V2 database.	['Samarendra Dandapat', 'Rohit Sinha', 'Mrinal Haloi']	2015-05-04	null	null	null	null	['severity-prediction']	['computer-vision']	[-2.25742534e-01 -1.81466877e-01 3.44042063e-01 -1.71937346e-01 -2.83065915e-01 -2.79609889e-01 2.59984005e-02 3.60553145e-01 -5.28231442e-01 7.38630056e-01 3.20150346e-01 -6.02011919e-01 -4.34780568e-01 -6.55366063e-01 1.54167876e-01 -7.61118233e-01 -1.56282812e-01 2.64626712e-01 6.82472944e-01 2.03368932e-01 5.80051005e-01 1.07607913e+00 -1.77218497e+00 7.24305093e-01 1.06204259e+00 1.19480336e+00 5.32745361e-01 1.28059614e+00 1.47846550e-01 7.42616236e-01 -4.15772229e-01 -6.63531944e-02 4.48791891e-01 -2.11218059e-01 -5.56858897e-01 2.14827284e-01 9.21794057e-01 -3.07023108e-01 -2.78628260e-01 6.71967626e-01 1.17440295e+00 -3.08176994e-01 1.27133226e+00 -4.87841249e-01 -4.45169151e-01 -6.74230754e-01 -5.65372229e-01 8.66063237e-01 -1.41050011e-01 1.63644627e-01 1.63879752e-01 -7.63963521e-01 7.64268160e-01 9.89570260e-01 5.37916362e-01 8.23030099e-02 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7808b8f5-7aee-46cd-a35c-b35eb33f771a	physics-based-shadow-image-decomposition-for	2012.13018	null	https://arxiv.org/abs/2012.13018v2	https://arxiv.org/pdf/2012.13018v2.pdf	Physics-based Shadow Image Decomposition for Shadow Removal	We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects from images. We then employ an inpainting network, I-Net, to further refine the results. We train and test our framework on the most challenging shadow removal dataset (ISTD). Our method improves the state-of-the-art in terms of root mean square error (RMSE) for the shadow area by 20\%. Furthermore, this decomposition allows us to formulate a patch-based weakly-supervised shadow removal method. This model can be trained without any shadow-free images (that are cumbersome to acquire) and achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. Last, we introduce SBU-Timelapse, a video shadow removal dataset for evaluating shadow removal methods.	['Dimitris Samaras', 'Hieu Le']	2020-12-23	null	null	null	null	['shadow-removal']	['computer-vision']	[ 6.70461893e-01 5.86396568e-02 5.32564104e-01 -2.57281840e-01 -4.00042534e-01 -1.51493773e-01 3.49234700e-01 -7.73621678e-01 -1.47130221e-01 8.06379914e-01 6.55700415e-02 -2.66607732e-01 5.65190554e-01 -5.46875238e-01 -9.74692822e-01 -1.17039204e+00 4.38146949e-01 2.86107123e-01 5.62003732e-01 -3.07702214e-01 9.25018080e-03 5.99901497e-01 -1.28425086e+00 1.13713734e-01 1.33303463e+00 9.82240200e-01 7.36158371e-01 6.85275614e-01 2.07912207e-01 7.56226599e-01 -7.76465237e-01 2.11669691e-02 4.44685340e-01 -4.48976487e-01 -3.91849093e-02 7.87609071e-02 6.57980323e-01 -9.42730784e-01 -8.12251449e-01 4.55730736e-01 5.61101854e-01 1.05338551e-01 6.62488341e-01 -1.07768953e+00 -6.88070476e-01 -2.99543232e-01 -6.38512313e-01 -2.26628929e-01 1.59903005e-01 1.14692070e-01 4.72586989e-01 -1.17213619e+00 6.70644343e-01 1.19295776e+00 9.81169820e-01 2.14227498e-01 -1.10263395e+00 -6.37536824e-01 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3b2334c0-c917-4db1-8376-9acc3299a553	decomposed-temporal-dynamic-cnn-efficient	2203.15277	null	https://arxiv.org/abs/2203.15277v2	https://arxiv.org/pdf/2203.15277v2.pdf	Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map	To extract accurate speaker information for text-independent speaker verification, temporal dynamic CNNs (TDY-CNNs) adapting kernels to each time bin was proposed. However, model size of TDY-CNN is too large and the adaptive kernel's degree of freedom is limited. To address these limitations, we propose decomposed temporal dynamic CNNs (DTDY-CNNs) which forms time-adaptive kernel by combining static kernel with dynamic residual based on matrix decomposition. Proposed DTDY-ResNet-34(x0.50) using attentive statistical pooling without data augmentation shows EER of 0.96%, which is better than other state-of-the-art methods. DTDY-CNNs are successful upgrade of TDY-CNNs, reducing the model size by 64% and enhancing the performance. We showed that DTDY-CNNs extract more accurate frame-level speaker embeddings as well compared to TDY-CNNs. Detailed behaviors of DTDY-ResNet-34(x0.50) on extraction of speaker information were analyzed using speaker activation map (SAM) produced by modified gradient-weighted class activation mapping (Grad-CAM) for speaker verification. DTDY-ResNet-34(x0.50) effectively extracts speaker information from not only formant frequencies but also high frequency information of unvoiced phonemes, thus explaining its outstanding performance on text-independent speaker verification.	['Yong-Hwa Park', 'Hyeonuk Nam', 'Seong-Hu Kim']	2022-03-29	null	null	null	null	['text-independent-speaker-verification']	['speech']	[-3.29318881e-01 -2.66801149e-01 1.75246537e-01 -3.71311665e-01 -8.85887921e-01 -4.20444191e-01 1.43143684e-01 -3.64499301e-01 -6.36319399e-01 3.18953037e-01 3.86524409e-01 -3.84038955e-01 3.73436630e-01 -3.07138592e-01 -3.62642258e-01 -8.35709751e-01 -3.16364348e-01 -2.96896607e-01 3.47910859e-02 -2.43685037e-01 -2.18901664e-01 5.42931318e-01 -1.36771345e+00 2.42755890e-01 7.69632459e-01 9.21390295e-01 7.02145025e-02 9.51751471e-01 -1.57077890e-02 4.65275228e-01 -9.67416644e-01 -1.03769727e-01 -2.96402834e-02 -3.64461154e-01 -3.75516683e-01 -3.68005484e-01 4.12373096e-01 -3.56071144e-01 -6.02545738e-01 8.67400408e-01 1.03437710e+00 4.40403640e-01 4.76494104e-01 -1.18767369e+00 -1.04631412e+00 8.53936851e-01 -4.30225283e-01 8.53758812e-01 1.80525538e-02 1.75344974e-01 6.99950874e-01 -1.23375750e+00 6.44932315e-02 1.44499218e+00 7.82660723e-01 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c503381b-a88a-4a44-a728-003f65eaedf5	a-novel-transferability-attention-neural	2009.09585	null	https://arxiv.org/abs/2009.09585v1	https://arxiv.org/pdf/2009.09585v1.pdf	A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition	The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (training) samples may lead to a negative influence because they are significant dissimilar with the target (test) samples. So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples. Furthermore, for an EEG sample, from the aspect of neuroscience, not all the brain regions of an EEG sample contains emotional information that can transferred to the test data effectively. Even some brain region data will make strong negative effect for learning the emotional classification model. Considering these two issues, in this paper, we propose a transferable attention neural network (TANN) for EEG emotion recognition, which learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively through local and global attention mechanism. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator. We conduct the extensive experiments on three public EEG emotional datasets. The results validate that the proposed model achieves the state-of-the-art performance.	['Guangming Shi', 'Boxun Fu', 'Yang Li', 'Wenming Zheng', 'Fu Li']	2020-09-21	null	null	null	null	['eeg-emotion-recognition']	['miscellaneous']	[-5.00102807e-03 -1.79257691e-01 3.72864097e-01 -8.30105841e-01 -3.50495964e-01 -1.30274385e-01 -3.10878642e-02 -1.38959676e-01 -2.51298338e-01 9.41153169e-01 -2.72516645e-02 3.61082554e-01 -9.69081447e-02 -6.26851499e-01 -6.73142731e-01 -9.81299996e-01 -2.33451873e-01 -2.29881257e-02 -2.45139807e-01 -1.26081169e-01 3.34747076e-01 3.52551907e-01 -1.41234159e+00 6.37324750e-01 1.12654364e+00 1.45847189e+00 1.89284876e-01 2.12311536e-01 6.71741785e-03 6.72328949e-01 -1.00771463e+00 -5.80705740e-02 -1.41935125e-01 -8.25617433e-01 -5.61179876e-01 -2.38278255e-01 -1.51586607e-01 -1.01343188e-02 -6.80191591e-02 1.42051125e+00 8.07849884e-01 1.03171118e-01 8.52539420e-01 -1.59412003e+00 -1.08207119e+00 2.89617449e-01 -5.47459900e-01 5.43366134e-01 3.48811895e-01 1.90053210e-02 4.97098505e-01 -1.05990052e+00 1.07867733e-01 8.53059351e-01 1.61020935e-01 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c47b763f-d45d-4e6f-9e09-2ae24c39b288	visualizing-global-explanations-of-point	2203.09505	null	https://arxiv.org/abs/2203.09505v2	https://arxiv.org/pdf/2203.09505v2.pdf	Visualizing Global Explanations of Point Cloud DNNs	In the field of autonomous driving and robotics, point clouds are showing their excellent real-time performance as raw data from most of the mainstream 3D sensors. Therefore, point cloud neural networks have become a popular research direction in recent years. So far, however, there has been little discussion about the explainability of deep neural networks for point clouds. In this paper, we propose a point cloud-applicable explainability approach based on a local surrogate model-based method to show which components contribute to the classification. Moreover, we propose quantitative fidelity validations for generated explanations that enhance the persuasive power of explainability and compare the plausibility of different existing point cloud-applicable explainability methods. Our new explainability approach provides a fairly accurate, more semantically coherent and widely applicable explanation for point cloud classification tasks. Our code is available at https://github.com/Explain3D/LIME-3D	['Hanxiao Tan']	2022-03-17	null	null	null	null	['point-cloud-classification']	['computer-vision']	[-3.10843259e-01 2.75142640e-01 -3.89467806e-01 -7.28678405e-01 -3.02683890e-01 -2.14447394e-01 7.92651832e-01 1.45471364e-01 3.52669120e-01 5.40093958e-01 -3.85535620e-02 -6.26670361e-01 -4.79142457e-01 -7.32985198e-01 -1.00688374e+00 -4.12640601e-01 2.05586344e-01 6.84486568e-01 -3.05897649e-03 -2.74842829e-01 2.70422906e-01 7.95209646e-01 -1.94699001e+00 1.11326508e-01 1.16418624e+00 9.30215418e-01 2.65672863e-01 2.57774979e-01 -2.64385074e-01 1.94183841e-01 -1.96049824e-01 -1.67115092e-01 2.39375487e-01 -8.48860294e-02 -5.18570006e-01 -2.31326938e-01 3.94748241e-01 -1.51865348e-01 -3.51534724e-01 9.12670970e-01 -2.12248601e-02 3.68420370e-02 6.85710788e-01 -1.93552303e+00 -1.01863444e+00 4.77594435e-01 -2.17767984e-01 -1.33338422e-01 -9.29913297e-02 1.86686113e-01 8.85926664e-01 -1.10562277e+00 1.88481748e-01 1.23472834e+00 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a55e8795-6258-4361-9d39-63871e0b52e0	naomi-non-autoregressive-multiresolution	1901.10946	null	https://arxiv.org/abs/1901.10946v3	https://arxiv.org/pdf/1901.10946v3.pdf	NAOMI: Non-Autoregressive Multiresolution Sequence Imputation	Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolutions using a divide-and-conquer strategy. We further enhance our model with adversarial training. When evaluated extensively on benchmark datasets from systems of both deterministic and stochastic dynamics. NAOMI demonstrates significant improvement in imputation accuracy (reducing average prediction error by 60% compared to autoregressive counterparts) and generalization for long range sequences.	['Yukai Liu', 'Yisong Yue', 'Rose Yu', 'Stephan Zheng', 'Eric Zhan']	2019-01-30	naomi-non-autoregressive-multiresolution-1	http://papers.nips.cc/paper/9302-naomi-non-autoregressive-multiresolution-sequence-imputation	http://papers.nips.cc/paper/9302-naomi-non-autoregressive-multiresolution-sequence-imputation.pdf	neurips-2019-12	['multivariate-time-series-imputation']	['time-series']	[ 3.22095841e-01 -4.31007355e-01 -1.21548414e-01 -3.04166734e-01 -1.21425533e+00 -5.53739250e-01 6.23132706e-01 -5.57352901e-01 -9.17713568e-02 1.30961454e+00 5.81006765e-01 -1.84053808e-01 -2.86322623e-01 -8.70208561e-01 -1.12278318e+00 -7.96005785e-01 -2.48650044e-01 4.14170921e-01 -8.49206522e-02 -2.34860331e-01 -1.12255923e-01 4.30386722e-01 -1.31735122e+00 4.50060964e-01 7.40939021e-01 7.29238391e-01 -2.28844807e-01 1.10083139e+00 2.35244393e-01 1.47931659e+00 -5.68155944e-01 -1.43936694e-01 1.36901915e-01 -2.48797506e-01 -2.43434995e-01 -4.07200247e-01 1.84486941e-01 -5.43145597e-01 -6.66525483e-01 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935fe2c8-a11f-447a-a309-b385fdf836ca	attentive-one-dimensional-heatmap-regression	2004.02108	null	https://arxiv.org/abs/2004.02108v7	https://arxiv.org/pdf/2004.02108v7.pdf	Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking	Although heatmap regression is considered a state-of-the-art method to locate facial landmarks, it suffers from huge spatial complexity and is prone to quantization error. To address this, we propose a novel attentive one-dimensional heatmap regression method for facial landmark localization. First, we predict two groups of 1D heatmaps to represent the marginal distributions of the x and y coordinates. These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates. With much lower spatial complexity, the proposed method can output high-resolution 1D heatmaps despite limited GPU memory, significantly alleviating the quantization error. Second, a co-attention mechanism is adopted to model the inherent spatial patterns existing in x and y coordinates, and therefore the joint distributions on the x and y axes are also captured. Third, based on the 1D heatmap structures, we propose a facial landmark detector capturing spatial patterns for landmark detection on an image; and a tracker further capturing temporal patterns with a temporal refinement mechanism for landmark tracking. Experimental results on four benchmark databases demonstrate the superiority of our method.	['Shangfei Wang', 'Enhong Chen', 'Xiaoping Chen', 'Shi Yin']	2020-04-05	null	null	null	null	['landmark-tracking']	['computer-vision']	[-2.78916627e-01 -1.78394392e-01 -2.37146780e-01 -5.05352736e-01 -8.01326513e-01 -7.07847178e-02 4.39498335e-01 5.60688786e-02 -2.75644273e-01 2.17268690e-01 -1.29432958e-02 1.00791410e-01 6.22986350e-03 -6.89357340e-01 -5.68264663e-01 -8.41033995e-01 -7.94461966e-02 1.71275765e-01 9.72067192e-02 2.89396942e-01 4.28002983e-01 5.74962199e-01 -1.57983851e+00 -2.76178122e-01 7.37457573e-01 1.35133183e+00 -1.05846614e-01 2.10066959e-01 -2.63904899e-01 1.96751326e-01 -3.27281922e-01 1.11445887e-02 6.88146800e-02 -2.33330801e-01 -2.73205906e-01 -1.60647616e-01 7.60943532e-01 -2.39934906e-01 -2.13022381e-01 1.10428631e+00 4.86151993e-01 2.65871082e-02 6.47269607e-01 -1.41985989e+00 -8.47174704e-01 -9.95856151e-02 -1.18912268e+00 -7.95969814e-02 3.39017242e-01 -4.42993976e-02 6.26165867e-01 -1.32105064e+00 3.43275100e-01 1.35556960e+00 8.63363504e-01 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755c2bbb-b8a7-4328-862d-80742262e01c	ur-channel-robust-synthetic-speech-detection	2107.12018	null	https://arxiv.org/abs/2107.12018v2	https://arxiv.org/pdf/2107.12018v2.pdf	UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021	In this paper, we present UR-AIR system submission to the logical access (LA) and the speech deepfake (DF) tracks of the ASVspoof 2021 Challenge. The LA and DF tasks focus on synthetic speech detection (SSD), i.e. detecting text-to-speech and voice conversion as spoofing attacks. Different from previous ASVspoof challenges, the LA task this year presents codec and transmission channel variability, while the new task DF presents general audio compression. Built upon our previous research work on improving the robustness of the SSD systems to channel effects, we propose a channel-robust synthetic speech detection system for the challenge. To mitigate the channel variability issue, we use an acoustic simulator to apply transmission codec, compression codec, and convolutional impulse responses to augmenting the original datasets. For the neural network backbone, we propose to use Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Networks (ECAPA-TDNN) as our primary model. We also incorporate one-class learning with channel-robust training strategies to further learn a channel-invariant speech representation. Our submission achieved EER 20.33% in the DF task; EER 5.46% and min-tDCF 0.3094 in the LA task.	['Zhiyao Duan', 'Ge Zhu', 'You Zhang', 'Xinhui Chen']	2021-07-26	null	null	null	null	['synthetic-speech-detection']	['audio']	[ 1.34471014e-01 -1.52493753e-02 -1.75963029e-01 -2.64629517e-02 -1.17068434e+00 -4.67887998e-01 4.44847375e-01 -1.02367416e-01 -2.59938270e-01 2.81032473e-01 4.32915092e-01 -1.11047435e+00 3.21068704e-01 -1.82009861e-01 -7.31048346e-01 -3.88772368e-01 -2.68728107e-01 -2.13285595e-01 2.19670057e-01 -1.02965541e-01 -1.79626375e-01 4.82438087e-01 -1.21206331e+00 5.61226130e-01 4.94200200e-01 1.36698294e+00 3.32305789e-01 1.38229156e+00 1.17541872e-01 7.00017214e-01 -1.13499081e+00 -1.78206757e-01 2.31004819e-01 -2.39288971e-01 -4.69899058e-01 -2.84477949e-01 2.48589680e-01 -6.63783967e-01 -1.11276007e+00 7.82342911e-01 1.18595147e+00 2.54569063e-03 3.97623509e-01 -1.33769047e+00 -2.31467232e-01 8.21611404e-01 -7.97417238e-02 3.91135067e-01 2.59954423e-01 3.63649338e-01 7.83183515e-01 -9.82253313e-01 9.60738584e-02 1.60191882e+00 7.21389651e-01 7.46259630e-01 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78663235-6759-45ca-ac60-a181baeccb57	transforming-musical-signals-through-a-genre	1706.09553	null	http://arxiv.org/abs/1706.09553v1	http://arxiv.org/pdf/1706.09553v1.pdf	Transforming Musical Signals through a Genre Classifying Convolutional Neural Network	Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.	['G. Ren', 'S. Geng', 'M. Ogihara']	2017-06-29	null	null	null	null	['genre-classification']	['computer-vision']	[ 5.39851069e-01 3.10338348e-01 7.09720477e-02 -2.59014100e-01 -2.59159803e-01 -6.60958588e-01 4.06254917e-01 -1.72255903e-01 -2.18542278e-01 4.72754747e-01 3.71316969e-01 4.48550105e-01 -1.96339726e-01 -1.08251667e+00 -6.85699642e-01 -7.82385290e-01 4.90903743e-02 3.12746406e-01 -2.40688309e-01 -2.57696450e-01 3.48110169e-01 4.08959448e-01 -1.89751923e+00 8.72261643e-01 4.61103886e-01 1.20485497e+00 2.87342697e-01 7.23492265e-01 -6.88225925e-02 7.94007182e-01 -8.96247327e-01 -2.90850133e-01 2.02164218e-01 -6.73954368e-01 -7.81083047e-01 -2.11469173e-01 -6.30751029e-02 1.27075672e-01 -2.99900323e-01 1.07626653e+00 4.71446812e-01 2.55553097e-01 4.03085411e-01 -9.59006906e-01 -5.58223605e-01 1.48947775e+00 2.96633035e-01 -8.46627355e-02 3.59210908e-01 2.61968672e-01 1.18489242e+00 -6.40456319e-01 7.58958340e-01 9.87068117e-01 5.77022076e-01 5.90141237e-01 -1.27767444e+00 -9.04668987e-01 -1.16758943e-01 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460c29de-0e93-4021-9ade-6dbe9b272aaa	cross-lingual-evidence-improves-monolingual	null	null	https://aclanthology.org/2021.acl-srw.32	https://aclanthology.org/2021.acl-srw.32.pdf	Cross-lingual Evidence Improves Monolingual Fake News Detection	Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose a new technique based on cross-lingual evidence (CE) that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature with several strong baselines on two multi-domain datasets of general-topic news and one newly fake COVID-19 news dataset showing that combining cross-lingual evidence with strong baselines such as RoBERTa yields significant improvements in fake news detection.	['Alexander Panchenko', 'Daryna Dementieva']	2021-08-01	null	null	null	acl-2021-5	['news-classification']	['natural-language-processing']	[-3.89270365e-01 3.07939685e-04 -4.95084137e-01 -1.51538640e-01 -9.78847742e-01 -6.71608090e-01 1.40256643e+00 2.99828976e-01 -2.39112213e-01 9.69387174e-01 2.82273501e-01 -3.14339310e-01 3.46986383e-01 -7.25727499e-01 -8.53362203e-01 -3.28133404e-01 3.71794373e-01 4.10146385e-01 6.58519387e-01 -6.98303699e-01 6.64709866e-01 3.24116737e-01 -1.20590603e+00 7.39953756e-01 9.79270160e-01 5.88868618e-01 -3.42101425e-01 -5.13368612e-03 -1.42267765e-02 8.09200585e-01 -1.11394739e+00 -1.03919792e+00 6.57529011e-02 -5.03336370e-01 -7.71934807e-01 -5.33816889e-02 5.60001493e-01 -3.04877788e-01 -1.71501830e-01 1.27724659e+00 2.02368706e-01 -5.61544359e-01 6.78917766e-01 -1.27292967e+00 -5.98961353e-01 7.66227841e-01 -5.18946052e-01 4.52234149e-01 3.40394974e-01 -2.94118047e-01 8.51917982e-01 -7.40003526e-01 1.07495391e+00 1.31324816e+00 7.46657491e-01 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b0304d18-559c-46a6-b7c3-015493cec72f	vulcan-solving-the-steiner-tree-problem-with	2111.10810	null	https://arxiv.org/abs/2111.10810v1	https://arxiv.org/pdf/2111.10810v1.pdf	Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning	Steiner Tree Problem (STP) in graphs aims to find a tree of minimum weight in the graph that connects a given set of vertices. It is a classic NP-hard combinatorial optimization problem and has many real-world applications (e.g., VLSI chip design, transportation network planning and wireless sensor networks). Many exact and approximate algorithms have been developed for STP, but they suffer from high computational complexity and weak worst-case solution guarantees, respectively. Heuristic algorithms are also developed. However, each of them requires application domain knowledge to design and is only suitable for specific scenarios. Motivated by the recently reported observation that instances of the same NP-hard combinatorial problem may maintain the same or similar combinatorial structure but mainly differ in their data, we investigate the feasibility and benefits of applying machine learning techniques to solving STP. To this end, we design a novel model Vulcan based on novel graph neural networks and deep reinforcement learning. The core of Vulcan is a novel, compact graph embedding that transforms highdimensional graph structure data (i.e., path-changed information) into a low-dimensional vector representation. Given an STP instance, Vulcan uses this embedding to encode its pathrelated information and sends the encoded graph to a deep reinforcement learning component based on a double deep Q network (DDQN) to find solutions. In addition to STP, Vulcan can also find solutions to a wide range of NP-hard problems (e.g., SAT, MVC and X3C) by reducing them to STP. We implement a prototype of Vulcan and demonstrate its efficacy and efficiency with extensive experiments using real-world and synthetic datasets.	['Qinqing Zhan', 'Qiao Xiang', 'Zong Yan', 'Haizhou Du']	2021-11-21	null	null	null	null	['steiner-tree-problem']	['graphs']	[ 9.48594734e-02 3.96589100e-01 -4.85051960e-01 -3.08435082e-01 -3.11023444e-01 -5.73959112e-01 -1.64355770e-01 2.06504613e-01 -1.00500159e-01 7.74588823e-01 -4.34153855e-01 -5.78307152e-01 -6.91485643e-01 -1.44083977e+00 -1.11138058e+00 -6.22538507e-01 -4.76273715e-01 8.24754477e-01 1.64547592e-01 -3.35736483e-01 1.16279446e-01 6.11957610e-01 -1.21020901e+00 -1.73978567e-01 8.29725325e-01 1.17521822e+00 2.64434576e-01 3.71083647e-01 -3.80948484e-01 3.21402162e-01 -4.40000623e-01 -4.39605206e-01 4.22849983e-01 -4.86566462e-02 -8.94052267e-01 3.95682454e-03 1.66241638e-02 5.29884771e-02 -6.86589897e-01 1.05302203e+00 2.90322065e-01 -7.37879351e-02 3.90958846e-01 -1.97925389e+00 -7.63300955e-01 8.23198020e-01 -6.70295417e-01 -9.95979533e-02 3.04261833e-01 1.03299916e-01 1.10076284e+00 -2.91901737e-01 4.75290447e-01 1.31051540e+00 5.51629364e-01 4.55620229e-01 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2b534832-51d4-46df-adbb-cf4b959c081a	change-point-detection-in-wind-turbine-scada	null	null	https://wes.copernicus.org/articles/5/1375/2020/	https://wes.copernicus.org/articles/5/1375/2020/wes-5-1375-2020.pdf	Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models	Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. Its predominant application is to monitor turbine condition without the need for additional sensing equipment. Most approaches apply semi-supervised anomaly detection methods, also called normal behaviour models, that require clean training data sets to establish healthy component baseline models. In practice, however, the presence of change points induced by malfunctions or maintenance actions poses a major challenge. Even though this problem is well described in literature, this contribution is the first to systematically evaluate and address the issue. A total of 600 signals from 33 turbines are analysed over an operational period of more than 2 years. During this time one-third of the signals were affected by change points, which highlights the necessity of an automated detection method. Kernel-based change-point detection methods have shown promising results in similar settings. We, therefore, introduce an appropriate SCADA data preprocessing procedure to ensure their feasibility and conduct comprehensive comparisons across several hyperparameter choices. The results show that the combination of Laplace kernels with a newly introduced bandwidth and regularisation-penalty selection heuristic robustly outperforms existing methods. More than 90 % of the signals were classified correctly regarding the presence or absence of change points, resulting in an F1 score of 0.86. For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0.96 and therefore without any significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data preprocessing, which is key for data-driven methods to reach their full potential. The algorithm is open source and its implementation in Python is publicly available.	['Simon Letzgus']	2020-10-27	null	null	null	wind-energy-science-2020-10	['supervised-anomaly-detection', 'semi-supervised-anomaly-detection']	['computer-vision', 'computer-vision']	[ 1.55060142e-01 -4.24147516e-01 2.14485854e-01 5.07311635e-02 -4.31604236e-01 -8.74018788e-01 5.78898311e-01 7.32745647e-01 -2.71423846e-01 6.01941049e-01 -5.29978931e-01 -4.03964877e-01 -4.90989655e-01 -6.07046545e-01 -1.21451803e-01 -9.91362691e-01 -4.87902373e-01 1.68012604e-02 4.34028208e-01 -1.95888668e-01 2.32330576e-01 8.05070281e-01 -1.89092505e+00 -3.37096959e-01 9.22778904e-01 9.84777868e-01 -1.23119857e-02 5.06573617e-01 2.70989001e-01 9.02094692e-02 -1.07021761e+00 2.88229465e-01 2.84371316e-01 -3.38668436e-01 -3.31369698e-01 1.85859110e-02 -1.66966617e-01 -1.21421739e-01 4.65061992e-01 9.78184581e-01 7.45361686e-01 2.30493248e-01 5.19093335e-01 -1.12504268e+00 3.99047852e-01 1.92276329e-01 -3.31063986e-01 5.24067938e-01 3.35055947e-01 3.47291976e-01 7.50060439e-01 -7.65063107e-01 5.98744974e-02 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a4b8ebcf-441e-426a-b473-185e4a2c5d6e	real-time-pose-and-shape-reconstruction-of	2106.08059	null	https://arxiv.org/abs/2106.08059v1	https://arxiv.org/pdf/2106.08059v1.pdf	Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera	We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an extensive list of favorable properties, namely it is marker-less, uses a single consumer-level depth camera, runs in real time, handles inter- and intra-hand collisions, and automatically adjusts to the user's hand shape. In order to achieve this, we embed a recent parametric hand pose and shape model and a dense correspondence predictor based on a deep neural network into a suitable energy minimization framework. For training the correspondence prediction network, we synthesize a two-hand dataset based on physical simulations that includes both hand pose and shape annotations while at the same time avoiding inter-hand penetrations. To achieve real-time rates, we phrase the model fitting in terms of a nonlinear least-squares problem so that the energy can be optimized based on a highly efficient GPU-based Gauss-Newton optimizer. We show state-of-the-art results in scenes that exceed the complexity level demonstrated by previous work, including tight two-hand grasps, significant inter-hand occlusions, and gesture interaction.	['Christian Theobalt', 'Dan Casas', 'Miguel A. Otaduy', 'Mickeal Verschoor', 'Oleksandr Sotnychenko', 'Florian Bernard', 'Micah Davis', 'Franziska Mueller']	2021-06-15	null	null	null	null	['physical-simulations']	['miscellaneous']	[ 3.77302691e-02 -2.21828863e-01 1.00301959e-01 -6.16404265e-02 -6.98154569e-01 -5.43701410e-01 3.65421288e-02 -1.69761032e-01 -5.48399210e-01 3.33882719e-01 -2.65345424e-01 -1.15628920e-01 -1.74033225e-01 -2.93339670e-01 -7.75068223e-01 -4.31042254e-01 -7.32859671e-02 1.21413267e+00 4.31252599e-01 -2.24446714e-01 2.77307123e-01 1.04143584e+00 -1.45486188e+00 -1.20438099e-01 5.26508689e-01 9.68189240e-01 4.44911391e-01 9.08079445e-01 3.77874702e-01 1.55031383e-01 -2.07358927e-01 -3.22375268e-01 6.67640090e-01 1.34195477e-01 -6.01435006e-01 7.40710199e-02 5.32990813e-01 -7.95057654e-01 -4.41985220e-01 5.76893091e-01 9.94746029e-01 2.43150771e-01 4.14643049e-01 -8.68399203e-01 1.39774773e-02 -3.00825946e-02 -6.94809914e-01 -5.94812214e-01 6.67417765e-01 4.90149587e-01 5.64299285e-01 -7.95557320e-01 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3.13725471e-01 1.80642318e-03 -2.33154821e+00 -8.86905730e-01 -8.35748911e-01 -6.28808737e-01 2.59169877e-01 2.75613427e-01 -8.99879098e-01 7.01660737e-02 -1.47683963e-01]	[6.603609561920166, -0.9152875542640686]
8624e8ae-a9dd-4d8a-9747-695b2e948157	fast-distributed-submodular-cover-public	null	null	http://papers.nips.cc/paper/6540-fast-distributed-submodular-cover-public-private-data-summarization	http://papers.nips.cc/paper/6540-fast-distributed-submodular-cover-public-private-data-summarization.pdf	Fast Distributed Submodular Cover: Public-Private Data Summarization	In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personalized recommender systems and online social services. Such systems have usually access to massive data generated by a large pool of users. A major fraction of the data is public and is visible to (and can be used for) all users. However, each user can also contribute some private data that should not be shared with other users to ensure her privacy. The goal is to provide a succinct summary of massive dataset, ideally as small as possible, from which customized summaries can be built for each user, i.e. it can contain elements from the public data (for diversity) and users' private data (for personalization). To formalize the above challenge, we assume that the scoring function according to which a user evaluates the utility of her summary satisfies submodularity, a widely used notion in data summarization applications. Thus, we model the data summarization targeted to each user as an instance of a submodular cover problem. However, when the data is massive it is infeasible to use the centralized greedy algorithm to find a customized summary even for a single user. Moreover, for a large pool of users, it is too time consuming to find such summaries separately. Instead, we develop a fast distributed algorithm for submodular cover, FASTCOVER, that provides a succinct summary in one shot and for all users. We show that the solution provided by FASTCOVER is competitive with that of the centralized algorithm with the number of rounds that is exponentially smaller than state of the art results. Moreover, we have implemented FASTCOVER with Spark to demonstrate its practical performance on a number of concrete applications, including personalized location recommendation, personalized movie recommendation, and dominating set on tens of millions of data points and varying number of users.	['Morteza Zadimoghaddam', 'Baharan Mirzasoleiman', 'Amin Karbasi']	2016-12-01	null	null	null	neurips-2016-12	['movie-recommendation', 'data-summarization']	['miscellaneous', 'miscellaneous']	[ 1.40181277e-02 4.74259943e-01 -2.97781199e-01 -2.89250761e-01 -1.02526677e+00 -9.14409220e-01 -1.08381189e-01 6.83871269e-01 -1.27833456e-01 9.81584370e-01 5.45471787e-01 2.00412422e-01 -2.48011321e-01 -9.04267192e-01 -7.79627621e-01 -7.07734466e-01 -3.00750379e-02 6.22034371e-01 6.01535738e-02 -2.23855913e-01 -5.79067841e-02 1.17306359e-01 -1.48057771e+00 2.24297747e-01 8.66792083e-01 8.86610210e-01 2.86978632e-01 4.10812408e-01 2.51266599e-01 7.95746595e-02 -5.80321670e-01 -5.59259355e-01 6.77753508e-01 -1.59814164e-01 -6.28219128e-01 3.66349518e-01 3.09330165e-01 -7.67962337e-01 -3.74236554e-01 1.14222014e+00 6.25919282e-01 2.57509530e-01 1.89985156e-01 -1.39271581e+00 -4.84126806e-01 1.06610835e+00 -6.89225078e-01 -1.43564612e-01 5.33665657e-01 -3.22450995e-01 1.45130873e+00 -3.65542620e-01 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170ee4c6-2cdb-4254-8027-fb42dfbd5c25	vrebert-a-simple-and-flexible-transformer-for	2206.09111	null	https://arxiv.org/abs/2206.09111v1	https://arxiv.org/pdf/2206.09111v1.pdf	VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection	Visual Relationship Detection (VRD) impels a computer vision model to 'see' beyond an individual object instance and 'understand' how different objects in a scene are related. The traditional way of VRD is first to detect objects in an image and then separately predict the relationship between the detected object instances. Such a disjoint approach is prone to predict redundant relationship tags (i.e., predicate) between the same object pair with similar semantic meaning, or incorrect ones that have a similar meaning to the ground truth but are semantically incorrect. To remedy this, we propose to jointly train a VRD model with visual object features and semantic relationship features. To this end, we propose VReBERT, a BERT-like transformer model for Visual Relationship Detection with a multi-stage training strategy to jointly process visual and semantic features. We show that our simple BERT-like model is able to outperform the state-of-the-art VRD models in predicate prediction. Furthermore, we show that by using the pre-trained VReBERT model, our model pushes the state-of-the-art zero-shot predicate prediction by a significant margin (+8.49 R@50 and +8.99 R@100).	['Moshiur Farazi', 'Yu Cui']	2022-06-18	null	null	null	null	['visual-relationship-detection']	['computer-vision']	[ 2.49854401e-01 3.37565154e-01 8.71488079e-02 -3.90404165e-01 -5.05414307e-01 -4.65440184e-01 7.12537348e-01 6.41974032e-01 -8.28424096e-02 1.09286450e-01 -1.38630107e-01 -2.28218630e-01 -9.45275947e-02 -8.65719318e-01 -9.58171725e-01 -2.35229775e-01 7.07935691e-02 7.71775961e-01 9.55610037e-01 -1.89405277e-01 1.54565930e-01 5.26724458e-01 -1.98047245e+00 6.79687142e-01 3.68436098e-01 1.15082812e+00 6.31446183e-01 5.21088243e-01 -1.06599681e-01 1.14635527e+00 -4.46989298e-01 -6.18252277e-01 -2.65253764e-02 -2.16334641e-01 -8.64559472e-01 1.68855369e-01 6.71962857e-01 -3.49993929e-02 -2.96427399e-01 9.24495101e-01 1.87391620e-02 2.28122965e-01 6.78541481e-01 -1.52539229e+00 -6.93455160e-01 2.69581348e-01 -6.89557016e-01 2.93110311e-01 6.21262372e-01 -1.35504484e-01 1.35415649e+00 -1.07245266e+00 8.23494375e-01 1.37998605e+00 5.06495595e-01 3.17097902e-01 -1.36793065e+00 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7.13628292e-01 2.29671836e-01 -1.06384099e+00 -1.98314711e-01 6.42249808e-02]	[10.275918006896973, 1.6510984897613525]
d2303093-74b6-45d6-a0dc-bbf06aff9304	a-role-for-prior-knowledge-in-statistical	2012.00538	null	https://arxiv.org/abs/2012.00538v1	https://arxiv.org/pdf/2012.00538v1.pdf	A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease	The transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great interest to clinical researchers. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), yielding equivalent or superior classification accuracy over other ML methods. Further, in applications with many features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different selection procedures. In the present study, we sought to investigate the use of automated and theoretically-guided feature selection techniques, and as well as the L-1 norm when applying different classification techniques for predicting conversion from MCI to AD in a highly characterized and studied sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We propose an alternative pre-selection technique that utilizes an efficient feature selection based on clinical knowledge of brain regions involved in AD. The present findings demonstrate how similar performance can be achieved using user-guided pre-selection versus algorithmic feature selection techniques. Finally, we compare the performance of a support vector machine (SVM) with that of logistic regression on multi-modal data from ADNI. The present findings show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers.	['Andrew R. Bender', 'Tapabrate Maiti', 'Zihuan Liu']	2020-11-28	null	null	null	null	['clinical-knowledge']	['miscellaneous']	[ 2.09509760e-01 -2.79762685e-01 -3.78873646e-01 -7.30389893e-01 -8.09672058e-01 -1.55199811e-01 4.75405276e-01 4.62862968e-01 -8.86267960e-01 1.05516422e+00 1.23626597e-01 -5.19941628e-01 -3.31126273e-01 -6.74294114e-01 -1.14835575e-01 -5.33596873e-01 -2.56708801e-01 7.19628990e-01 2.10941121e-01 1.51405320e-01 2.53051221e-01 5.48959374e-01 -1.54770184e+00 3.59752387e-01 1.05270970e+00 1.00826991e+00 3.35663766e-01 2.10090414e-01 -1.66258328e-02 2.11276293e-01 -3.76239330e-01 -2.35878989e-01 9.89294723e-02 -4.02631938e-01 -5.75826168e-01 -1.17012501e-01 5.70642412e-01 -3.86819273e-01 1.22648329e-01 7.88792610e-01 6.54575229e-01 5.55848069e-02 9.71819580e-01 -1.04373145e+00 -3.93718064e-01 2.80727625e-01 -1.33894444e-01 6.27955675e-01 2.05883011e-01 1.39448762e-01 8.98035467e-01 -8.36714566e-01 5.81345022e-01 1.03797925e+00 8.34684312e-01 3.45153183e-01 -1.41682374e+00 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3.00673097e-02 1.15680084e-01 -1.13002408e+00 -5.83524585e-01 2.19571739e-02]	[14.167820930480957, -1.7490606307983398]
eb75dbe9-81a1-4b2d-9fc4-03c43eaed73b	a-novel-structured-argumentation-framework	2306.15500	null	https://arxiv.org/abs/2306.15500v1	https://arxiv.org/pdf/2306.15500v1.pdf	A novel structured argumentation framework for improved explainability of classification tasks	This paper presents a novel framework for structured argumentation, named extend argumentative decision graph ($xADG$). It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The $xADG$ framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structure, resulting in more concise argumentation graphs that may be easier for users to understand. The study presents a methodology for construction of $xADGs$ and evaluates their size and predictive capacity for classification tasks of varying magnitudes. Resulting $xADGs$ achieved strong (balanced) accuracy, which was accomplished through an input decision tree, while also reducing the average number of supports needed to reach a conclusion. The results further indicated that it is possible to construct plausibly understandable $xADGs$ that outperform other techniques for building $ADGs$ in terms of predictive capacity and overall size. In summary, the study suggests that $xADG$ represents a promising framework to developing more concise argumentative models that can be used for classification tasks and knowledge discovery, acquisition, and refinement.	['Luca Longo', 'Lucas Rizzo']	2023-06-27	null	null	null	null	['abstract-argumentation', 'abstract-argumentation']	['natural-language-processing', 'reasoning']	[-9.99796763e-02 1.44010544e+00 -5.98814487e-01 -4.65098053e-01 -2.68439621e-01 -6.63591623e-01 7.29949296e-01 1.03319263e+00 3.43884267e-02 1.16288245e+00 -3.46814990e-02 -1.40474260e+00 -8.42392623e-01 -1.31086659e+00 -6.06729567e-01 -9.83750075e-02 -3.09856594e-01 4.24887151e-01 1.34893715e-01 -5.77829838e-01 5.91061413e-01 2.41036803e-01 -1.72430992e+00 6.92041099e-01 9.62759912e-01 1.12679744e+00 -3.84138703e-01 1.72713101e-01 -3.83360505e-01 1.18492639e+00 -7.92814016e-01 -1.08453810e+00 -1.34448931e-01 -7.12868497e-02 -1.35271609e+00 -5.21417260e-01 -1.31976128e-01 -5.42290546e-02 6.28701031e-01 7.21881926e-01 1.24701718e-02 -2.74020344e-01 7.94984639e-01 -1.62936413e+00 -4.29719388e-01 1.50919151e+00 -2.35980839e-01 1.11424914e-02 9.23835993e-01 -4.65661556e-01 1.42792678e+00 -6.60020888e-01 5.83943069e-01 1.64879191e+00 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49d3df84-cf4f-42e9-9692-18e3e7b22a6c	learning-to-draw-emergent-communication	2106.02067	null	https://arxiv.org/abs/2106.02067v2	https://arxiv.org/pdf/2106.02067v2.pdf	Learning to Draw: Emergent Communication through Sketching	Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solve tasks. Existing research has focused on language, with a learned communication channel transmitting sequences of discrete tokens between the agents. In this work, we explore a visual communication channel between agents that are allowed to draw with simple strokes. Our agents are parameterised by deep neural networks, and the drawing procedure is differentiable, allowing for end-to-end training. In the framework of a referential communication game, we demonstrate that agents can not only successfully learn to communicate by drawing, but with appropriate inductive biases, can do so in a fashion that humans can interpret. We hope to encourage future research to consider visual communication as a more flexible and directly interpretable alternative of training collaborative agents.	['Jonathon Hare', 'Daniela Mihai']	2021-06-03	null	http://proceedings.neurips.cc/paper/2021/hash/39d0a8908fbe6c18039ea8227f827023-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/39d0a8908fbe6c18039ea8227f827023-Paper.pdf	neurips-2021-12	['prehistory']	['miscellaneous']	[ 7.94983283e-02 4.84117061e-01 2.66882420e-01 -2.39040300e-01 1.49145290e-01 -9.79720354e-01 1.44442260e+00 2.81045921e-02 -5.94171762e-01 8.14695835e-01 4.91923541e-01 -6.76590443e-01 2.10095927e-01 -9.06964898e-01 -7.15809822e-01 -4.98673975e-01 -5.59214473e-01 7.12076604e-01 -3.91939908e-01 -3.24172199e-01 4.09955502e-01 3.59328479e-01 -1.12459731e+00 3.16539943e-01 5.75236261e-01 9.48514789e-02 1.27394095e-01 1.22752905e+00 -2.68094242e-01 1.36167157e+00 -1.11686933e+00 -4.94800895e-01 3.89321260e-02 -8.68339837e-01 -9.52772558e-01 8.00878480e-02 -5.11745624e-02 -5.26110709e-01 -1.67341754e-01 4.17645454e-01 2.51802683e-01 1.00603856e-01 9.23840225e-01 -1.17069340e+00 -1.45405650e+00 1.08991075e+00 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82b4a68e-c5fb-4fba-bb50-f25675233ed8	vn-transformer-rotation-equivariant-attention	2206.04176	null	https://arxiv.org/abs/2206.04176v3	https://arxiv.org/pdf/2206.04176v3.pdf	VN-Transformer: Rotation-Equivariant Attention for Vector Neurons	Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations. Vector Neurons (VN) is a recently developed framework offering a simple yet effective approach for deriving rotation-equivariant analogs of standard machine learning operations by extending one-dimensional scalar neurons to three-dimensional "vector neurons." We introduce a novel "VN-Transformer" architecture to address several shortcomings of the current VN models. Our contributions are: $(i)$ we derive a rotation-equivariant attention mechanism which eliminates the need for the heavy feature preprocessing required by the original Vector Neurons models; $(ii)$ we extend the VN framework to support non-spatial attributes, expanding the applicability of these models to real-world datasets; $(iii)$ we derive a rotation-equivariant mechanism for multi-scale reduction of point-cloud resolution, greatly speeding up inference and training; $(iv)$ we show that small tradeoffs in equivariance ($\epsilon$-approximate equivariance) can be used to obtain large improvements in numerical stability and training robustness on accelerated hardware, and we bound the propagation of equivariance violations in our models. Finally, we apply our VN-Transformer to 3D shape classification and motion forecasting with compelling results.	['Ben Sapp', 'Nigamaa Nayakanti', 'Rami Al-Rfou', 'Carlton Downey', 'Serge Assaad']	2022-06-08	null	null	null	null	['3d-shape-retrieval']	['computer-vision']	[ 5.94295897e-02 -2.14899123e-01 -1.30363926e-01 -3.20678294e-01 -7.30336368e-01 -5.86389244e-01 5.43824971e-01 -4.37892199e-01 -3.56467426e-01 3.79821002e-01 -2.98752449e-03 -6.93423569e-01 -1.69307634e-01 -6.58188522e-01 -1.12779593e+00 -6.37265980e-01 -1.77882388e-01 1.41256049e-01 -2.63890829e-02 -4.69362199e-01 4.94220734e-01 1.20298767e+00 -1.63894176e+00 -2.61235852e-02 3.96249115e-01 1.18179977e+00 -6.10438362e-02 6.85290515e-01 2.22475454e-01 2.29612783e-01 -1.60914019e-01 1.08649552e-01 6.75586045e-01 3.83419901e-01 -7.04541385e-01 -7.47734308e-02 7.71639228e-01 -2.29844525e-01 -4.82326776e-01 9.37996805e-01 4.62955266e-01 5.73686898e-01 8.15253675e-01 -1.10139334e+00 -8.17452669e-01 2.39564568e-01 -5.57795584e-01 2.61665225e-01 -2.80311257e-01 1.48612291e-01 9.51545715e-01 -1.16636252e+00 6.99557960e-01 1.48419559e+00 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88cb6766-307e-441d-b9a2-5fd8a8ad2b78	heterogeneous-neuronal-and-synaptic-dynamics	2302.11618	null	https://arxiv.org/abs/2302.11618v1	https://arxiv.org/pdf/2302.11618v1.pdf	Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles	This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity), leading to improved classification and prediction performance. We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity. The analytical results motivate Heterogeneous RSNN design using Bayesian optimization to determine heterogeneity in neurons and synapses to improve $\mathcal{E}$, defined as the ratio of spiking activity and memory capacity. The empirical results on time series classification and prediction tasks show that optimized HRSNN increases performance and reduces spiking activity compared to a homogeneous RSNN.	['Saibal Mukhopadhyay', 'Biswadeep Chakraborty']	2023-02-22	null	null	null	null	['time-series-classification']	['time-series']	[ 3.99261981e-01 -2.42988631e-01 -1.67546347e-02 3.12246084e-02 -1.44001961e-01 -4.28751469e-01 1.65623143e-01 -2.76569307e-01 -5.91881752e-01 1.18833518e+00 -1.29507840e-01 9.25444886e-02 -3.69445920e-01 -6.42251790e-01 -7.93502867e-01 -1.41173172e+00 -2.14759871e-01 -2.50930130e-03 6.37909710e-01 6.71408847e-02 6.12342596e-01 7.13817418e-01 -1.84361374e+00 2.55264252e-01 6.30847692e-01 1.17813277e+00 5.24106741e-01 3.85620385e-01 5.25591485e-02 6.47122562e-01 -5.66068232e-01 3.53820801e-01 3.15346420e-01 -4.52441990e-01 1.56846330e-01 -3.87607008e-01 -3.34207833e-01 5.70996940e-01 -5.08745909e-01 7.60291100e-01 6.17755890e-01 -6.80175424e-02 8.93418550e-01 -1.11028206e+00 -3.26477885e-01 8.46865416e-01 -1.49223372e-01 7.03029454e-01 -8.28220785e-01 2.55956650e-01 5.42038202e-01 -5.05333543e-01 3.86754423e-01 5.94123900e-01 9.20398116e-01 6.16795480e-01 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3064b594-489b-403c-93f8-28bc83f14a92	measuring-the-privacy-leakage-via-graph	2302.04373	null	https://arxiv.org/abs/2302.04373v1	https://arxiv.org/pdf/2302.04373v1.pdf	Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)	In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA). We propose a GRA that recovers a graph's adjacency matrix from the representations via a graph decoder that minimizes the reconstruction loss between the partial graph and the reconstructed graph. We study three types of representations that are trained on the graph, i.e., representations output from graph convolutional network (GCN), graph attention network (GAT), and our proposed simplicial neural network (SNN) via a higher-order combinatorial Laplacian. Unlike the first two types of representations that only encode pairwise relationships, the third type of representation, i.e., SNN outputs, encodes higher-order interactions (e.g., homological features) between nodes. We find that the SNN outputs reveal the lowest privacy-preserving ability to defend the GRA, followed by those of GATs and GCNs, which indicates the importance of building more private representations with higher-order node information that could defend the potential threats, such as GRAs.	['Victor S. Sheng', 'Keyi Lu', 'Kun Zhang', 'Huixin Zhan']	2023-02-08	null	null	null	null	['graph-reconstruction']	['graphs']	[ 4.84441906e-01 8.82699251e-01 9.10854563e-02 6.07651286e-03 -4.12727058e-01 -1.14662492e+00 4.54676062e-01 1.40960500e-01 1.70891300e-01 6.63810492e-01 2.32642964e-01 -7.70168841e-01 2.57144906e-02 -1.35872602e+00 -1.27366996e+00 -7.53959835e-01 -3.42530191e-01 -2.92488723e-04 -1.44561604e-01 -3.69215548e-01 2.86911905e-01 6.11281753e-01 -1.02984226e+00 1.33539781e-01 8.28036249e-01 6.88459694e-01 -2.99693972e-01 4.56597805e-01 2.20009789e-01 5.87902725e-01 -3.41339141e-01 -8.38801324e-01 6.19990230e-01 -4.28243548e-01 -6.10954583e-01 -3.62418085e-01 2.81303674e-01 -2.75934875e-01 -1.11447322e+00 1.53742456e+00 1.18236318e-01 -1.65529072e-01 7.20432222e-01 -1.56604671e+00 -1.25039935e+00 6.84564054e-01 -5.41567266e-01 -6.63446262e-02 4.35930133e-01 3.49468172e-01 1.16531694e+00 -3.18853766e-01 7.00098991e-01 1.37822151e+00 5.73612869e-01 5.10607958e-01 -1.44705462e+00 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-8.81476700e-01 7.19615638e-01 4.74305123e-01 -4.07578200e-01 -8.92894685e-01 -7.07402170e-01]	[6.063649654388428, 7.128176212310791]
f4d5f5d1-a24f-40e5-98aa-0e3e4d1ed577	automatic-player-identification-in-dota-2	2008.12401	null	https://arxiv.org/abs/2008.12401v1	https://arxiv.org/pdf/2008.12401v1.pdf	Automatic Player Identification in Dota 2	Dota 2 is a popular, multiplayer online video game. Like many online games, players are mostly anonymous, being tied only to online accounts which can be readily obtained, sold and shared between multiple people. This makes it difficult to track or ban players who exhibit unwanted behavior online. In this paper, we present a machine learning approach to identify players based a `digital fingerprint' of how they play the game, rather than by account. We use data on mouse movements, in-game statistics and game strategy extracted from match replays and show that for best results, all of these are necessary. We are able to obtain an accuracy of prediction of 95\% for the problem of predicting if two different matches were played by the same player.	['Sizhe Yuen', 'Oliver Don', 'John D. Thomson']	2020-08-27	null	null	null	null	['dota-2']	['playing-games']	[-2.65101314e-01 -8.16549361e-02 -2.60557830e-01 1.17475323e-01 -5.91075957e-01 -1.05320847e+00 3.85562927e-01 2.70024426e-02 -7.71138191e-01 7.17179060e-01 -1.66726291e-01 -5.26334822e-01 -4.01659012e-02 -9.77247238e-01 -2.99851000e-01 -1.25642166e-01 -2.95721740e-01 5.94816685e-01 9.46635842e-01 -1.34222224e-01 4.69772756e-01 2.77678937e-01 -1.37486410e+00 3.36258739e-01 1.42438546e-01 8.80979598e-01 -1.75787866e-01 1.13352203e+00 2.75194086e-02 1.33164012e+00 -6.68971062e-01 -1.23633397e+00 5.33313155e-01 -7.32746840e-01 -6.77359283e-01 -3.90898257e-01 -1.14277162e-01 -5.83424628e-01 -6.00891829e-01 9.21201587e-01 3.22938979e-01 1.91245854e-01 4.26354378e-01 -1.32259488e+00 2.48094827e-01 6.09287798e-01 -6.52717829e-01 4.91737515e-01 9.55110669e-01 -1.12212904e-01 1.12349629e+00 -1.27633587e-01 7.60757744e-01 4.34314460e-01 1.03885829e+00 3.36318046e-01 -1.17595410e+00 -8.38016033e-01 -5.11973202e-01 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d8253507-6d6f-4f2e-b9bc-9b716ec8d0df	towards-modality-agnostic-person-re	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.pdf	Towards Modality-Agnostic Person Re-Identification With Descriptive Query	Person re-identification (ReID) with descriptive query (text or sketch) provides an important supplement for general image-image paradigms, which is usually studied in a single cross-modality matching manner, e.g., text-to-image or sketch-to-photo. However, without a camera-captured photo query, it is uncertain whether the text or sketch is available or not in practical scenarios. This motivates us to study a new and challenging modality-agnostic person re-identification problem. Towards this goal, we propose a unified person re-identification (UNIReID) architecture that can effectively adapt to cross-modality and multi-modality tasks. Specifically, UNIReID incorporates a simple dual-encoder with task-specific modality learning to mine and fuse visual and textual modality information. To deal with the imbalanced training problem of different tasks in UNIReID, we propose a task-aware dynamic training strategy in terms of task difficulty, adaptively adjusting the training focus. Besides, we construct three multi-modal ReID datasets by collecting the corresponding sketches from photos to support this challenging task. The experimental results on three multi-modal ReID datasets show that our UNIReID greatly improves the retrieval accuracy and generalization ability on different tasks and unseen scenarios.	['Ding Jiang', 'Mang Ye', 'Cuiqun Chen']	2023-01-01	null	null	null	cvpr-2023-1	['person-re-identification']	['computer-vision']	[ 1.96551234e-01 -7.15543747e-01 -1.56439498e-01 -4.66703117e-01 -8.38416755e-01 -6.17733955e-01 8.83384824e-01 -1.99169755e-01 -6.46892667e-01 5.29971540e-01 2.63265908e-01 2.71566331e-01 -1.50604725e-01 -4.65112031e-01 -6.06586277e-01 -5.80020130e-01 7.85432577e-01 5.88740587e-01 -1.53145134e-01 -1.17973043e-02 1.87626868e-01 3.80018592e-01 -1.65931082e+00 1.91537321e-01 6.38635516e-01 7.98942924e-01 2.01924443e-01 3.91586334e-01 -1.01704493e-01 1.58799887e-01 -3.94308150e-01 -1.01632810e+00 3.65747869e-01 -1.34655982e-01 -5.77045918e-01 2.81990349e-01 1.05244517e+00 -5.75210214e-01 -7.45862126e-01 9.57543075e-01 9.32427526e-01 2.00647742e-01 6.24939561e-01 -1.47818351e+00 -1.00265360e+00 2.10503995e-01 -8.43001187e-01 3.10216486e-01 4.89964604e-01 7.94509947e-02 5.89824378e-01 -1.10665786e+00 4.34785575e-01 1.49002254e+00 5.77549815e-01 8.67514491e-01 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bec8551f-18bc-4bf5-b41a-92a2384cf4e3	i-vector-text-independent-speaker	null	null	https://aclanthology.org/O13-1016	https://aclanthology.org/O13-1016.pdf	結合I-Vector 及深層神經網路之語者驗證系統 (Text-independent Speaker Verification using a Hybrid I-Vector/DNN Approach) [In Chinese]	null	['Wen-Tsung Chang', 'Chia-Wei Liao', 'Kai-Hsuan Chan', 'Shao-Hua Cheng', 'Yun-Fan Chang', 'Yu Tsao']	2013-10-01	i-vector-text-independent-speaker-1	https://aclanthology.org/O13-1016	https://aclanthology.org/O13-1016.pdf	roclingijclclp-2013-10	['text-independent-speaker-verification']	['speech']	[-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 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ce8a7e69-52e1-47c0-97d3-6cd7d05d0cd8	towards-unsupervised-speech-to-text	1811.01307	null	http://arxiv.org/abs/1811.01307v1	http://arxiv.org/pdf/1811.01307v1.pdf	Towards Unsupervised Speech-to-Text Translation	We present a framework for building speech-to-text translation (ST) systems using only monolingual speech and text corpora, in other words, speech utterances from a source language and independent text from a target language. As opposed to traditional cascaded systems and end-to-end architectures, our system does not require any labeled data (i.e., transcribed source audio or parallel source and target text corpora) during training, making it especially applicable to language pairs with very few or even zero bilingual resources. The framework initializes the ST system with a cross-modal bilingual dictionary inferred from the monolingual corpora, that maps every source speech segment corresponding to a spoken word to its target text translation. For unseen source speech utterances, the system first performs word-by-word translation on each speech segment in the utterance. The translation is improved by leveraging a language model and a sequence denoising autoencoder to provide prior knowledge about the target language. Experimental results show that our unsupervised system achieves comparable BLEU scores to supervised end-to-end models despite the lack of supervision. We also provide an ablation analysis to examine the utility of each component in our system.	['Wei-Hung Weng', 'Yu-An Chung', 'Schrasing Tong', 'James Glass']	2018-11-04	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 2.21990570e-01 1.78647354e-01 -3.20237964e-01 -5.94402075e-01 -1.44831026e+00 -7.49270439e-01 6.73482060e-01 -2.74413556e-01 -3.86251211e-01 6.82667494e-01 4.66075063e-01 -7.48028278e-01 7.72595525e-01 -3.30456644e-01 -8.74341905e-01 -4.31811780e-01 3.62328172e-01 8.66895914e-01 -2.27472544e-01 -3.78225178e-01 -3.81616294e-01 -3.95964608e-02 -8.43855739e-01 3.58331352e-01 6.83895409e-01 5.41968703e-01 4.49489504e-01 6.37980759e-01 -1.88968197e-01 6.35576546e-01 -6.29849553e-01 -5.22989333e-01 3.20335180e-01 -9.63976562e-01 -8.82777393e-01 3.25332135e-01 1.86301544e-01 -3.82426053e-01 -3.32912713e-01 9.06293690e-01 6.01471484e-01 -8.57862681e-02 3.57770115e-01 -9.33682680e-01 -8.16171527e-01 1.01248586e+00 -2.79107332e-01 1.17901541e-01 4.36637282e-01 -1.93405841e-02 1.01654232e+00 -1.40949380e+00 6.67289197e-01 1.34816623e+00 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1d22315e-178a-4f8c-a1d8-c3ae6cff9279	gexse-generative-explanatory-sensor-system-an	2306.15857	null	https://arxiv.org/abs/2306.15857v1	https://arxiv.org/pdf/2306.15857v1.pdf	GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces	We introduce GeXSe (Generative Explanatory Sensor System), a novel framework designed to extract interpretable sensor-based and vision domain features from non-invasive smart space sensors. We combine these to provide a comprehensive explanation of sensor-activation patterns in activity recognition tasks. This system leverages advanced machine learning architectures, including transformer blocks, Fast Fourier Convolution (FFC), and diffusion models, to provide a more detailed understanding of sensor-based human activity data. A standout feature of GeXSe is our unique Multi-Layer Perceptron (MLP) with linear, ReLU, and normalization layers, specially devised for optimal performance on small datasets. It also yields meaningful activation maps to explain sensor-based activation patterns. The standard approach is based on a CNN model, which our MLP model outperforms.GeXSe offers two types of explanations: sensor-based activation maps and visual domain explanations using short videos. These methods offer a comprehensive interpretation of the output from non-interpretable sensor data, thereby augmenting the interpretability of our model. Utilizing the Frechet Inception Distance (FID) for evaluation, it outperforms established methods, improving baseline performance by about 6\%. GeXSe also achieves a high F1 score of up to 0.85, demonstrating precision, recall, and noise resistance, marking significant progress in reliable and explainable smart space sensing systems.	['Jorge Ortiz', 'Murtadha Aldeer', 'Viswa Vijeth Ramesh', 'Nandana Pai', 'Yuan Sun']	2023-06-28	null	null	null	null	['activity-recognition', 'human-activity-recognition', 'human-activity-recognition']	['computer-vision', 'computer-vision', 'time-series']	[ 6.81063473e-01 4.41477388e-01 -3.34913164e-01 -3.68285030e-01 -4.99430060e-01 -5.79586983e-01 6.00579679e-01 -9.64904670e-03 4.63317223e-02 5.16874611e-01 6.48489475e-01 -3.14351231e-01 -3.36470306e-01 -3.58318746e-01 -7.68375397e-01 -6.56211257e-01 -2.52481848e-01 -1.91964403e-01 -3.11314732e-01 7.04722404e-02 1.62079513e-01 6.15576148e-01 -1.60208833e+00 6.09822094e-01 5.38220823e-01 1.54772592e+00 -1.02401055e-01 9.26064670e-01 1.76503599e-01 8.70446682e-01 -6.52100384e-01 1.57118097e-01 -7.30222417e-03 -1.61998495e-01 -4.03183401e-01 -1.14809699e-01 3.30479354e-01 -4.00242656e-01 -3.99581403e-01 3.03680032e-01 2.39381641e-01 -1.88565016e-01 6.44030213e-01 -1.68475628e+00 -1.02189469e+00 3.93253833e-01 -7.79565722e-02 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9dde3202-a1b8-475c-9b55-d3f039f080fb	attention-based-3d-object-reconstruction-from	2008.04738	null	https://arxiv.org/abs/2008.04738v1	https://arxiv.org/pdf/2008.04738v1.pdf	Attention-based 3D Object Reconstruction from a Single Image	Recently, learning-based approaches for 3D reconstruction from 2D images have gained popularity due to its modern applications, e.g., 3D printers, autonomous robots, self-driving cars, virtual reality, and augmented reality. The computer vision community has applied a great effort in developing functions to reconstruct the full 3D geometry of objects and scenes. However, to extract image features, they rely on convolutional neural networks, which are ineffective in capturing long-range dependencies. In this paper, we propose to substantially improve Occupancy Networks, a state-of-the-art method for 3D object reconstruction. For such we apply the concept of self-attention within the network's encoder in order to leverage complementary input features rather than those based on local regions, helping the encoder to extract global information. With our approach, we were capable of improving the original work in 5.05% of mesh IoU, 0.83% of Normal Consistency, and more than 10X the Chamfer-L1 distance. We also perform a qualitative study that shows that our approach was able to generate much more consistent meshes, confirming its increased generalization power over the current state-of-the-art.	['Nathan Gavenski', 'Rodrigo Barros', 'Felipe Tasoniero', 'Eduardo Pooch', 'Andrey Salvi']	2020-08-11	null	null	null	null	['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image']	['computer-vision', 'computer-vision']	[-3.48979654e-03 3.32009643e-01 6.47862628e-02 -2.44402662e-01 -6.05135381e-01 -2.87687510e-01 5.79729855e-01 -7.02723414e-02 -1.28451303e-01 4.95543480e-01 6.19875118e-02 -3.94191081e-03 -1.64155304e-01 -1.01169157e+00 -1.19231498e+00 -2.27916703e-01 -7.43002370e-02 7.16489971e-01 3.60417396e-01 -3.66809428e-01 2.48088449e-01 1.02281821e+00 -1.94949245e+00 2.34566242e-01 5.60293078e-01 1.21940780e+00 3.36466461e-01 3.37793052e-01 -9.22066495e-02 5.44656634e-01 -1.62922949e-01 -9.99296531e-02 2.09610835e-01 -2.71467585e-02 -6.69685543e-01 2.22600270e-02 6.48657203e-01 -4.34943348e-01 -5.50514281e-01 7.65276849e-01 3.75783920e-01 -9.07933116e-02 6.29832029e-01 -9.52609420e-01 -7.93225169e-01 2.18557671e-01 -4.95986313e-01 -1.01829872e-01 4.93358880e-01 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eb739eac-56b0-43b9-96ea-6b0f4f647287	integrating-geometric-control-into-text-to	2306.04607	null	https://arxiv.org/abs/2306.04607v4	https://arxiv.org/pdf/2306.04607v4.pdf	Integrating Geometric Control into Text-to-Image Diffusion Models for High-Quality Detection Data Generation via Text Prompt	Diffusion models have attracted significant attention due to their remarkable ability to create content and generate data for tasks such as image classification. However, the usage of diffusion models to generate high-quality object detection data remains an underexplored area, where not only the image-level perceptual quality but also geometric conditions such as bounding boxes and camera views are essential. Previous studies have utilized either copy-paste synthesis or layout-to-image (L2I) generation with specifically designed modules to encode semantic layouts. In this paper, we propose GeoDiffusion, a simple framework that can flexibly translate various geometric conditions into text prompts and empower the pre-trained text-to-image (T2I) diffusion models for high-quality detection data generation. Unlike previous L2I methods, our GeoDiffusion is able to encode not only bounding boxes but also extra geometric conditions such as camera views in self-driving scenes. Extensive experiments demonstrate GeoDiffusion outperforms previous L2I methods while maintaining 4x training time faster. To the best of our knowledge, this is the first work to adopt diffusion models for layout-to-image generation with geometric conditions and demonstrate that L2I-generated images can be beneficial for improving the performance of object detectors.	['Dit-yan Yeung', 'Zhenguo Li', 'Lanqing Hong', 'Zhe Chen', 'Enze Xie', 'Kai Chen']	2023-06-07	null	null	null	null	['layout-to-image-generation']	['computer-vision']	[ 3.41363043e-01 -6.87683672e-02 2.05913365e-01 -3.19305539e-01 -4.87422705e-01 -7.05839336e-01 9.41196620e-01 -5.37680686e-02 -4.44986783e-02 3.22425157e-01 -5.92542067e-02 -4.81631070e-01 1.70388073e-01 -1.08883679e+00 -8.71630251e-01 -4.61767703e-01 2.75438726e-01 3.59490603e-01 7.10757554e-01 -2.30816349e-01 5.64809561e-01 7.06114650e-01 -1.82605004e+00 5.29048800e-01 9.29223120e-01 8.38610232e-01 6.62980855e-01 9.22758460e-01 -2.87189811e-01 5.86644769e-01 -9.54245627e-01 -2.29725346e-01 3.94314021e-01 -4.45675343e-01 -2.75802672e-01 2.60730773e-01 9.05843258e-01 -7.45625734e-01 -2.17949897e-01 8.79611433e-01 6.84816658e-01 -1.67048737e-01 7.91608989e-01 -1.37496817e+00 -1.16873848e+00 4.30311710e-01 -7.36755729e-01 1.18823968e-01 3.27531993e-01 6.47386909e-01 6.55996084e-01 -1.01454973e+00 8.33269536e-01 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3319d64a-3937-452b-b5bc-7441c2c9dec0	inesc-id-a-regression-model-for-large-scale	null	null	https://aclanthology.org/S15-2102	https://aclanthology.org/S15-2102.pdf	INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction	null	['Wang Ling', 'Ramon Astudillo', 'Silvio Amir', 'Isabel Trancoso', 'Mario J. Silva', 'Bruno Martins']	2015-06-01	null	null	null	semeval-2015-6	['twitter-sentiment-analysis']	['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 4.49454933e-02 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00 7.20913768e-01 1.26888943e+00 8.66221786e-01 5.44149756e-01 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e6274458-14b3-4f15-85fb-f363ed0f9b0d	dynamic-epistemic-logic-with-asp-updates	1905.10621	null	https://arxiv.org/abs/1905.10621v1	https://arxiv.org/pdf/1905.10621v1.pdf	Dynamic Epistemic Logic with ASP Updates: Application to Conditional Planning	Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has proved to be very successful for epistemic reasoning in planning tasks. In this logic, the agent's knowledge is captured by modal epistemic operators whereas the system evolution is described in terms of (some subset of) dynamic logic modalities in which actions are usually represented as semantic objects called event models. In this paper, we study a variant of DEL, that wecall DEL[ASP], where actions are syntactically described by using an Answer Set Programming (ASP) representation instead of event models. This representation directly inherits high level expressive features like indirect effects, qualifications, state constraints, defaults, or recursive fluents that are common in ASP descriptions of action domains. Besides, we illustrate how this approach can be applied for obtaining conditional plans in single-agent, partially observable domains where knowledge acquisition may be represented as indirect effects of actions.	['Luis Fariñas del Cerro', 'Jorge Fandinno', 'Pedro Cabalar']	2019-05-25	null	null	null	null	['epistemic-reasoning']	['miscellaneous']	[ 1.56951427e-01 1.04471004e+00 -9.63051394e-02 -3.87846470e-01 -1.59118980e-01 -8.45011115e-01 1.28036761e+00 2.53306895e-01 -2.24576861e-01 1.13788497e+00 3.30308914e-01 -2.85696179e-01 -4.60627079e-01 -1.25757074e+00 -6.94401264e-01 -5.37562072e-01 -2.63029873e-01 5.98653972e-01 8.47391248e-01 -5.11263728e-01 -2.13217244e-01 4.22949374e-01 -1.55874622e+00 3.63417566e-01 4.32390720e-01 5.28865099e-01 -2.37222061e-01 3.19301784e-01 -3.72818738e-01 1.69432163e+00 -3.59108061e-01 -5.59263647e-01 -2.59498984e-01 -4.45201308e-01 -1.58603287e+00 1.25374570e-01 -5.89825690e-01 -1.49171606e-01 6.27569854e-02 1.20021331e+00 -2.35079035e-01 4.98540215e-02 5.03474474e-01 -1.53833973e+00 -2.08322808e-01 1.11470938e+00 2.12820455e-01 -4.52473074e-01 1.21105063e+00 2.00923547e-01 1.00031567e+00 -1.76249996e-01 8.75349700e-01 1.59956467e+00 1.66353136e-01 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95c5282a-fa52-4c42-a8db-1d5073a808f8	adversarial-defense-via-neural-oscillation	2211.02223	null	https://arxiv.org/abs/2211.02223v1	https://arxiv.org/pdf/2211.02223v1.pdf	Adversarial Defense via Neural Oscillation inspired Gradient Masking	Spiking neural networks (SNNs) attract great attention due to their low power consumption, low latency, and biological plausibility. As they are widely deployed in neuromorphic devices for low-power brain-inspired computing, security issues become increasingly important. However, compared to deep neural networks (DNNs), SNNs currently lack specifically designed defense methods against adversarial attacks. Inspired by neural membrane potential oscillation, we propose a novel neural model that incorporates the bio-inspired oscillation mechanism to enhance the security of SNNs. Our experiments show that SNNs with neural oscillation neurons have better resistance to adversarial attacks than ordinary SNNs with LIF neurons on kinds of architectures and datasets. Furthermore, we propose a defense method that changes model's gradients by replacing the form of oscillation, which hides the original training gradients and confuses the attacker into using gradients of 'fake' neurons to generate invalid adversarial samples. Our experiments suggest that the proposed defense method can effectively resist both single-step and iterative attacks with comparable defense effectiveness and much less computational costs than adversarial training methods on DNNs. To the best of our knowledge, this is the first work that establishes adversarial defense through masking surrogate gradients on SNNs.	['Yilei Zhang', 'Chunming Jiang']	2022-11-04	null	null	null	null	['adversarial-defense']	['adversarial']	[ 3.18029553e-01 -1.58263847e-01 3.88776183e-01 -1.43801883e-01 4.83898409e-02 -8.04263294e-01 4.49855059e-01 -5.10407865e-01 -6.90986335e-01 9.18637395e-01 -2.72889167e-01 -2.97444940e-01 2.95114219e-01 -7.56454527e-01 -8.82613301e-01 -9.07080173e-01 1.49732279e-02 -2.09145606e-01 6.55271411e-01 -3.55948657e-01 5.44497192e-01 8.43512595e-01 -1.10005927e+00 3.47092003e-02 8.22017372e-01 9.43095922e-01 -2.71608949e-01 3.91896278e-01 8.06640461e-02 4.50832844e-01 -1.01030898e+00 -2.80251920e-01 4.27190214e-01 -4.00390387e-01 -1.42694026e-01 -8.48076582e-01 2.09975630e-01 -2.71208972e-01 -8.22372854e-01 1.59547853e+00 6.32984281e-01 -2.77197450e-01 3.62888128e-01 -1.49593568e+00 -8.82106543e-01 7.14698553e-01 -2.83252388e-01 5.32689035e-01 -2.16566756e-01 6.61921561e-01 1.19023077e-01 -2.47095287e-01 2.73625493e-01 1.11432350e+00 5.75886607e-01 1.45511544e+00 -1.28435433e+00 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1.15149595e-01 2.66572803e-01 -4.77176875e-01 -7.27255940e-01 -3.79889905e-01]	[5.572777271270752, 7.819549083709717]
a625af99-35b3-43bc-81b1-cf6b25a5d758	hifi-gan-high-fidelity-denoising-and	2006.05694	null	https://arxiv.org/abs/2006.05694v2	https://arxiv.org/pdf/2006.05694v2.pdf	HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks	Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to transform recorded speech to sound as though it had been recorded in a studio. We use an end-to-end feed-forward WaveNet architecture, trained with multi-scale adversarial discriminators in both the time domain and the time-frequency domain. It relies on the deep feature matching losses of the discriminators to improve the perceptual quality of enhanced speech. The proposed model generalizes well to new speakers, new speech content, and new environments. It significantly outperforms state-of-the-art baseline methods in both objective and subjective experiments.	['Adam Finkelstein', 'Zeyu Jin', 'Jiaqi Su']	2020-06-10	null	null	null	null	['speech-dereverberation']	['speech']	[ 2.60852277e-01 -5.61143272e-03 5.27500093e-01 -2.53231466e-01 -1.19444799e+00 -6.75006211e-01 1.93829224e-01 -5.02851903e-01 -2.36359268e-01 6.04173601e-01 6.51978493e-01 -5.96038103e-02 3.04938525e-01 -5.66114843e-01 -8.53573024e-01 -5.02559841e-01 -9.27827358e-02 -2.55924642e-01 -1.95525512e-01 -3.39443952e-01 -4.18160528e-01 2.64462888e-01 -1.05534887e+00 4.06557739e-01 7.06234038e-01 8.67668808e-01 -1.58182070e-01 1.18004060e+00 6.78916276e-01 5.68826139e-01 -1.21024203e+00 -5.19667745e-01 3.12177569e-01 -6.55378759e-01 -3.68070245e-01 -1.41716167e-01 4.78325307e-01 -5.61367631e-01 -1.08335900e+00 1.03551698e+00 1.11983228e+00 4.26541209e-01 4.78018612e-01 -8.66844475e-01 -1.05015481e+00 5.89558840e-01 -3.93895656e-02 2.20439598e-01 4.70856547e-01 2.32927710e-01 7.10258663e-01 -1.04695082e+00 7.66530260e-02 1.19000423e+00 8.60467911e-01 5.64868867e-01 -1.15178168e+00 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-7.46239126e-01 3.85570765e-01 1.20219481e+00 -2.34743118e-01 -7.20859826e-01 3.10441017e-01]	[15.228394508361816, 6.006433963775635]
b00945be-a473-4030-97df-304b4856ec43	the-larger-they-are-the-harder-they-fail	2305.15507	null	https://arxiv.org/abs/2305.15507v1	https://arxiv.org/pdf/2305.15507v1.pdf	The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Python	Large Language Models (LLMs) have successfully been applied to code generation tasks, raising the question of how well these models understand programming. Typical programming languages have invariances and equivariances in their semantics that human programmers intuitively understand and exploit, such as the (near) invariance to the renaming of identifiers. We show that LLMs not only fail to properly generate correct Python code when default function names are swapped, but some of them even become more confident in their incorrect predictions as the model size increases, an instance of the recently discovered phenomenon of Inverse Scaling, which runs contrary to the commonly observed trend of increasing prediction quality with increasing model size. Our findings indicate that, despite their astonishing typical-case performance, LLMs still lack a deep, abstract understanding of the content they manipulate, making them unsuitable for tasks that statistically deviate from their training data, and that mere scaling is not enough to achieve such capability.	['Shay B. Cohen', 'Ioannis Konstas', 'Fazl Barez', 'Antonio Valerio Miceli-Barone']	2023-05-24	null	null	null	null	['code-generation']	['computer-code']	[ 1.72537252e-01 3.53530467e-01 -1.04176290e-01 -5.61605096e-01 -3.49916108e-02 -7.30127633e-01 7.39736438e-01 3.50953043e-01 -2.12693483e-01 3.92312884e-01 2.23034799e-01 -8.52137804e-01 2.41420016e-01 -9.77068126e-01 -1.08641791e+00 3.17507237e-02 -7.85625875e-02 1.81517169e-01 2.02590540e-01 -3.87791425e-01 5.61863899e-01 -1.64074823e-02 -1.73345852e+00 4.30317491e-01 1.19797635e+00 3.38995345e-02 9.06484500e-02 7.18158960e-01 -4.48020369e-01 9.84312356e-01 -2.66074240e-01 -5.40424764e-01 2.17305049e-02 -2.18239158e-01 -8.53275239e-01 -4.25752282e-01 4.77793485e-01 -2.22792789e-01 6.78388625e-02 1.20909357e+00 -1.69617787e-01 -3.78562123e-01 3.89875859e-01 -1.38718235e+00 -9.80371833e-01 1.02163732e+00 -3.81449878e-01 -5.02883866e-02 4.80293930e-01 4.37225819e-01 1.19479668e+00 -5.03963530e-01 5.69811165e-01 1.23078620e+00 9.61059570e-01 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aaa23feb-1514-4af6-aad9-ed6bd871aa51	on-the-generalization-of-learned-structured	2304.13001	null	https://arxiv.org/abs/2304.13001v1	https://arxiv.org/pdf/2304.13001v1.pdf	On the Generalization of Learned Structured Representations	Despite tremendous progress over the past decade, deep learning methods generally fall short of human-level systematic generalization. It has been argued that explicitly capturing the underlying structure of data should allow connectionist systems to generalize in a more predictable and systematic manner. Indeed, evidence in humans suggests that interpreting the world in terms of symbol-like compositional entities may be crucial for intelligent behavior and high-level reasoning. Another common limitation of deep learning systems is that they require large amounts of training data, which can be expensive to obtain. In representation learning, large datasets are leveraged to learn generic data representations that may be useful for efficient learning of arbitrary downstream tasks. This thesis is about structured representation learning. We study methods that learn, with little or no supervision, representations of unstructured data that capture its hidden structure. In the first part of the thesis, we focus on representations that disentangle the explanatory factors of variation of the data. We scale up disentangled representation learning to a novel robotic dataset, and perform a systematic large-scale study on the role of pretrained representations for out-of-distribution generalization in downstream robotic tasks. The second part of this thesis focuses on object-centric representations, which capture the compositional structure of the input in terms of symbol-like entities, such as objects in visual scenes. Object-centric learning methods learn to form meaningful entities from unstructured input, enabling symbolic information processing on a connectionist substrate. In this study, we train a selection of methods on several common datasets, and investigate their usefulness for downstream tasks and their ability to generalize out of distribution.	['Andrea Dittadi']	2023-04-25	null	null	null	null	['systematic-generalization']	['reasoning']	[ 4.35638517e-01 5.05285621e-01 -3.42138439e-01 -6.67269170e-01 -1.14601061e-01 -6.56260669e-01 8.46017182e-01 2.81902552e-01 -1.19615726e-01 6.32813931e-01 3.73353928e-01 -3.22821856e-01 -4.73697573e-01 -9.34184670e-01 -9.73631740e-01 -4.25327212e-01 -1.51979299e-02 6.73350394e-01 -1.06981486e-01 -5.24842322e-01 1.79271519e-01 8.24913263e-01 -1.59963202e+00 6.94152594e-01 5.40516138e-01 6.94976628e-01 3.42079461e-01 3.15131843e-01 -2.68974602e-02 9.55580950e-01 -6.07095182e-01 -1.45312369e-01 2.78130293e-01 -4.48266298e-01 -1.01324761e+00 -1.00946836e-01 4.27394956e-01 -1.67968109e-01 -4.11470324e-01 6.30957365e-01 2.27324273e-02 3.43384683e-01 1.03505695e+00 -1.26181948e+00 -1.14756536e+00 9.80800509e-01 -4.41117510e-02 2.53866941e-01 2.19660833e-01 2.91020095e-01 1.33435035e+00 -6.65994287e-01 1.01098073e+00 1.45925689e+00 5.76180220e-01 8.20532739e-01 -1.76496935e+00 -4.86488849e-01 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fcca35b7-bd05-40f4-a11d-8631ce075fb7	instructed-diffuser-with-temporal-condition	2306.04875	null	https://arxiv.org/abs/2306.04875v1	https://arxiv.org/pdf/2306.04875v1.pdf	Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning	Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models have also shown strong competitiveness in reinforcement learning (RL) by formulating decision-making as sequential generation. However, incorporating temporal information of sequential data and utilizing it to guide diffusion models to perform better generation is still an open challenge. In this paper, we take one step forward to investigate controllable generation with temporal conditions that are refined from temporal information. We observe the importance of temporal conditions in sequential generation in sufficient explorative scenarios and provide a comprehensive discussion and comparison of different temporal conditions. Based on the observations, we propose an effective temporally-conditional diffusion model coined Temporally-Composable Diffuser (TCD), which extracts temporal information from interaction sequences and explicitly guides generation with temporal conditions. Specifically, we separate the sequences into three parts according to time expansion and identify historical, immediate, and prospective conditions accordingly. Each condition preserves non-overlapping temporal information of sequences, enabling more controllable generation when we jointly use them to guide the diffuser. Finally, we conduct extensive experiments and analysis to reveal the favorable applicability of TCD in offline RL tasks, where our method reaches or matches the best performance compared with prior SOTA baselines.	['DaCheng Tao', 'Yi Chang', 'Lichao Sun', 'Li Shen', 'Hechang Chen', 'Siyuan Guo', 'Sili Huang', 'Yanchao Sun', 'Jifeng Hu']	2023-06-08	null	null	null	null	['offline-rl']	['playing-games']	[ 2.96665609e-01 -1.85214072e-01 -5.44772267e-01 -1.49778232e-01 -3.95086437e-01 -7.10003793e-01 1.26986766e+00 -1.37481689e-01 -6.20300889e-01 7.54141629e-01 6.11464143e-01 -3.43013078e-01 -1.98069483e-01 -7.38921642e-01 -3.90730292e-01 -8.81230950e-01 -1.88853025e-01 2.72971243e-01 7.65212923e-02 -4.69438642e-01 1.72553927e-01 3.18741858e-01 -1.46407425e+00 3.87822807e-01 1.03648841e+00 7.34996438e-01 5.29566467e-01 5.96755326e-01 9.02012736e-02 1.13468659e+00 -6.63482249e-01 5.33870459e-02 2.63719171e-01 -8.17304432e-01 -6.57430112e-01 7.42974691e-03 -2.54490912e-01 -5.77203691e-01 -2.72761166e-01 5.38429856e-01 6.85717404e-01 5.29243231e-01 7.12673664e-01 -1.03847146e+00 -9.75922585e-01 1.01732814e+00 -5.84515214e-01 3.45401913e-01 6.16148114e-01 7.16465175e-01 9.50962842e-01 -4.56864417e-01 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ccc243c7-1301-4f15-984e-0029907eb0ac	ensemble-creation-via-anchored-regularization	2210.06829	null	https://arxiv.org/abs/2210.06829v1	https://arxiv.org/pdf/2210.06829v1.pdf	Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction	Aspect Based Sentiment Analysis is the most granular form of sentiment analysis that can be performed on the documents / sentences. Besides delivering the most insights at a finer grain, it also poses equally daunting challenges. One of them being the shortage of labelled data. To bring in value right out of the box for the text data being generated at a very fast pace in today's world, unsupervised aspect-based sentiment analysis allows us to generate insights without investing time or money in generating labels. From topic modelling approaches to recent deep learning-based aspect extraction models, this domain has seen a lot of development. One of the models that we improve upon is ABAE that reconstructs the sentences as a linear combination of aspect terms present in it, In this research we explore how we can use information from another unsupervised model to regularize ABAE, leading to better performance. We contrast it with baseline rule based ensemble and show that the ensemble methods work better than the individual models and the regularization based ensemble performs better than the rule-based one.	['Manu Joseph', 'Pulah Dhandekar']	2022-10-13	null	null	null	null	['aspect-extraction', 'aspect-based-sentiment-analysis']	['natural-language-processing', 'natural-language-processing']	[ 1.23205952e-01 4.67061102e-01 -1.13166131e-01 -5.70358753e-01 -6.59387648e-01 -5.10946333e-01 8.11670899e-01 4.31570590e-01 -2.60427713e-01 6.59338295e-01 6.08105302e-01 -3.39410275e-01 -2.89710648e-02 -1.03243625e+00 -3.77996951e-01 -7.10463762e-01 4.10472542e-01 7.31546938e-01 2.46255682e-03 -6.19979143e-01 3.47078830e-01 1.06073819e-01 -1.72797036e+00 4.80972648e-01 6.23399734e-01 9.60327566e-01 -1.48168907e-01 4.55931604e-01 -8.23064148e-01 8.86160135e-01 -5.20411670e-01 -6.01681352e-01 1.18020378e-01 -3.65447402e-01 -6.97118759e-01 2.52521038e-01 -7.24767372e-02 1.82936043e-01 3.01457703e-01 7.56035089e-01 2.53033519e-01 1.21025473e-01 7.93166041e-01 -1.03340042e+00 -3.14436078e-01 6.21303260e-01 -6.45070493e-01 -2.71767825e-01 3.17498952e-01 -3.62057745e-01 1.09230530e+00 -8.11122894e-01 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6be3de31-c4ce-4394-b1e1-574ad799d5d2	execution-based-code-generation-using-deep	2301.13816	null	https://arxiv.org/abs/2301.13816v2	https://arxiv.org/pdf/2301.13816v2.pdf	Execution-based Code Generation using Deep Reinforcement Learning	The utilization of programming language (PL) models, pretrained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting specific sequence-level features of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that combines pretrained PL models with Proximal Policy Optimization (PPO) deep reinforcement learning and employs execution feedback as the external source of knowledge into the model optimization. PPOCoder is transferable across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, improving the success rate of compilation and functional correctness over different PLs. Our code can be found at https://github.com/reddy-lab-code-research/PPOCoder .	['Chandan K. Reddy', 'Sindhu Tipirneni', 'Aneesh Jain', 'Parshin Shojaee']	2023-01-31	null	null	null	null	['code-translation', 'program-synthesis']	['computer-code', 'computer-code']	[-4.28055972e-02 1.19777493e-01 -4.50325489e-01 -1.54089585e-01 -9.43931341e-01 -5.97270131e-01 4.28529352e-01 1.35144740e-01 1.27122894e-01 5.05922318e-01 8.67284164e-02 -6.62267148e-01 1.89754486e-01 -8.47485423e-01 -9.26007569e-01 -1.41405150e-01 1.03653058e-01 1.06800802e-01 -2.10817173e-01 -2.39381343e-01 5.32064974e-01 1.19349629e-01 -1.33999705e+00 3.63253415e-01 1.33839226e+00 4.38875496e-01 3.94269705e-01 6.93178833e-01 -2.94295877e-01 9.47018981e-01 -4.36148196e-01 -3.75403970e-01 1.37257591e-01 -3.00873280e-01 -7.55926669e-01 -1.69453293e-01 -6.28772229e-02 -1.02388881e-01 -6.69944063e-02 1.16570437e+00 3.10183555e-01 -1.92303896e-01 2.25770384e-01 -1.30230176e+00 -1.01778686e+00 1.10713565e+00 -5.06025851e-01 -2.44579718e-01 3.86299640e-01 5.60598135e-01 1.16246593e+00 -8.26533318e-01 4.57223773e-01 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24e6e5c9-127e-4b1b-a734-928694b58199	land-cover-segmentation-with-sparse	2306.16252	null	https://arxiv.org/abs/2306.16252v1	https://arxiv.org/pdf/2306.16252v1.pdf	Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery	Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.	['Fabrizio Dominici', 'Claudio Rossi', 'Luca Barco', 'Edoardo Arnaudo', 'Marco Galatola']	2023-06-28	null	null	null	null	['management']	['miscellaneous']	[ 1.51551872e-01 3.41651104e-02 -3.28656495e-01 -3.80039662e-01 -1.01432693e+00 -8.46023023e-01 4.53603834e-01 5.25006115e-01 -4.36255336e-01 7.57383168e-01 -3.16954106e-02 -3.05899411e-01 2.48090878e-01 -1.18148565e+00 -6.86432600e-01 -5.43938816e-01 7.23585337e-02 6.92863703e-01 6.96379662e-01 -1.93271130e-01 -1.09637223e-01 4.01531607e-01 -1.29996443e+00 -2.25848570e-01 1.36592937e+00 1.03698480e+00 3.91007543e-01 3.17440301e-01 -6.38131917e-01 9.22478456e-03 -5.18063962e-01 -2.77396381e-01 3.68000984e-01 -1.10516123e-01 -8.18298221e-01 -8.62555206e-02 2.73198783e-01 -1.61829516e-01 1.87874794e-01 1.32029986e+00 2.81923920e-01 2.36925259e-01 5.91517746e-01 -1.24528146e+00 1.00661434e-01 5.57605982e-01 -7.45890021e-01 1.03513291e-02 -5.34103326e-02 -2.53470391e-02 8.51972461e-01 -5.12292266e-01 5.41096926e-01 1.05661714e+00 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9aca0d4c-912a-4932-8c37-d1042ee8258e	emergent-communication-under-competition	2101.10276	null	https://arxiv.org/abs/2101.10276v1	https://arxiv.org/pdf/2101.10276v1.pdf	Emergent Communication under Competition	The literature in modern machine learning has only negative results for learning to communicate between competitive agents using standard RL. We introduce a modified sender-receiver game to study the spectrum of partially-competitive scenarios and show communication can indeed emerge in a competitive setting. We empirically demonstrate three key takeaways for future research. First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms. Second, we highlight the difference between communication and manipulation and extend previous metrics of communication to the competitive case. Third, we investigate the negotiation game where previous work failed to learn communication between independent agents (Cao et al., 2018). We show that, in this setting, both agents must benefit from communication for it to emerge; and, with a slight modification to the game, we demonstrate successful communication between competitive agents. We hope this work overturns misconceptions and inspires more research in competitive emergent communication.	['Aaron Courville', 'Angeliki Lazaridou', 'Travis LaCroix', 'Michael Noukhovitch']	2021-01-25	null	null	null	null	['misconceptions']	['miscellaneous']	[ 1.96138814e-01 6.35856152e-01 1.46167070e-01 1.63347483e-01 -5.59619486e-01 -9.48303998e-01 8.08584452e-01 1.75209761e-01 -6.75895393e-01 1.24034703e+00 -1.03285313e-02 -1.93812251e-01 -3.92915338e-01 -5.91294110e-01 -8.28698814e-01 -9.75798368e-01 -9.13079441e-01 4.01383251e-01 -2.10226968e-01 -6.99307501e-01 7.38469884e-03 1.14500068e-01 -1.24338782e+00 -1.17604584e-01 6.24809206e-01 3.63004833e-01 2.25505177e-02 1.08964288e+00 3.79451513e-01 1.03901470e+00 -9.42646742e-01 -2.45139495e-01 7.11102784e-01 -9.47116554e-01 -9.04422462e-01 1.87653098e-02 -3.93003136e-01 -5.88312335e-02 9.39783547e-03 7.29230165e-01 5.77399433e-01 -1.15414441e-01 5.70620179e-01 -1.90504360e+00 -5.47442853e-01 1.48658085e+00 -5.57589114e-01 5.18219955e-02 2.85063833e-01 9.95222554e-02 1.20990133e+00 -1.21089593e-02 6.18189871e-01 1.15943968e+00 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8.10054123e-01 -7.56583393e-01 -4.71224397e-01 -8.65483522e-01 -5.74810565e-01 -6.24404214e-02 6.23550832e-01 -5.55372834e-01 -1.03204215e+00 -4.96659487e-01]	[3.8875296115875244, 2.0451467037200928]
8616f380-25ff-449c-b7cb-ec3c156e1bdf	lijunyi-at-semeval-2019-task-9-an-attention	null	null	https://aclanthology.org/S19-2212	https://aclanthology.org/S19-2212.pdf	Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums	In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can be classified into suggestion and non-suggestion classes. In this task, we combine the attention mechanism with the LSTM model, which is the final system we submitted. The final submission achieves 14th place in Task 9, SubTask A with the accuracy of 0.6776. After the challenge, we train a series of neural network models such as convolutional neural network(CNN), TextCNN, long short-term memory(LSTM) and C-LSTM. Finally, we make an ensemble on the predictions of these models and get a better result.	['Junyi Li']	2019-06-01	null	null	null	semeval-2019-6	['suggestion-mining']	['natural-language-processing']	[-1.45868555e-01 4.69374269e-01 -2.09418640e-01 -6.72801375e-01 -3.51777226e-01 -2.11186963e-03 8.48279297e-01 4.52508852e-02 -6.14324331e-01 6.51721954e-01 3.40441823e-01 -1.01545715e+00 1.71953410e-01 -5.54933310e-01 -6.42564595e-01 -1.54572308e-01 1.16495397e-02 3.31296653e-01 1.68719366e-01 -4.12616521e-01 7.40984440e-01 -3.03443968e-01 -1.43258965e+00 9.96416926e-01 6.05489910e-01 1.22692668e+00 -5.36157796e-03 5.86437047e-01 -4.92874324e-01 5.37454903e-01 -4.70981658e-01 -4.43044901e-01 9.02739614e-02 -1.13210253e-01 -1.00496280e+00 -1.50714323e-01 4.41451967e-01 -3.04240972e-01 9.94270388e-03 8.32028210e-01 2.04977855e-01 6.70972466e-01 7.40618050e-01 -5.80303192e-01 -9.67072845e-01 1.37711859e+00 -5.56055963e-01 2.22290799e-01 1.59520850e-01 -3.57718855e-01 9.36882854e-01 -1.72796094e+00 2.56662071e-01 1.18697870e+00 5.59043050e-01 6.81510866e-01 -6.50654256e-01 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4.45058476e-03 1.04272135e-01 -9.29169059e-02 -2.36118153e-01 -3.37641239e-01]	[10.92123031616211, 7.499916076660156]
a3425567-069d-45dc-bb6f-b46abba9c0cd	document-level-relation-extraction-with-5	2212.10171	null	https://arxiv.org/abs/2212.10171v1	https://arxiv.org/pdf/2212.10171v1.pdf	Document-level Relation Extraction with Relation Correlations	Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges.	['Xiang Wan', 'Lu Liu', 'Benyou Wang', 'Tao Peng', 'Ridong Han']	2022-12-20	null	null	null	null	['document-level-relation-extraction']	['natural-language-processing']	[-0.24839471 0.22286598 -0.78734386 -0.51585287 -0.6375822 -0.5728418 0.67292666 0.6109512 -0.26286173 0.7651231 0.8293629 -0.16457714 -0.5450312 -0.9014157 -0.24467212 -0.41214782 -0.2679314 0.69430006 0.10359047 -0.49688938 -0.02161562 0.5159665 -1.1268258 0.3961969 0.80218124 1.0388381 -0.17931321 0.14227012 -0.18686742 1.0208379 -0.20629641 -0.8800027 -0.13999496 -0.03843091 -1.2822506 -0.19638106 0.07022149 0.11301325 -0.49412733 0.88005614 0.329997 0.18994226 0.9892153 -1.0987536 -1.1497786 0.9977828 -0.5324735 0.42166382 0.3567135 -0.7503087 2.0415635 -1.1172316 0.80531514 1.2102069 0.66353697 0.18680087 -0.9284796 -0.65442586 0.20468882 0.55862105 -1.5723727 -0.22305128 0.7612396 -0.44789696 1.2861931 0.11073159 0.42128175 0.9465141 -0.10134801 0.52962494 0.81708914 -0.4921829 -0.42091244 0.14539386 0.5192455 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26e26906-2bb5-41a0-9e71-1a3f53b033d8	encoder-decoder-network-with-guided	2212.05936	null	https://arxiv.org/abs/2212.05936v2	https://arxiv.org/pdf/2212.05936v2.pdf	Encoder-Decoder Network with Guided Transmission Map: Architecture	An insight into the architecture of the Encoder-Decoder Network with Guided Transmission Map (EDN-GTM), a novel and effective single image dehazing scheme, is presented in this paper. The EDN-GTM takes a conventional RGB hazy image in conjunction with the corresponding transmission map estimated by the dark channel prior (DCP) approach as inputs of the network. The EDN-GTM adopts an enhanced structure of U-Net developed for dehazing tasks and the resulting EDN-GDM has shown state-of-the-art performances on benchmark dehazing datasets in terms of PSNR and SSIM metrics. In order to give an in-depth understanding of the well-designed architecture which largely contributes to the success of the EDN-GTM, extensive experiments and analysis from selecting the core structure of the scheme to investigating advanced network designs are presented in this paper.	['Dong-Chul Park', 'Le-Anh Tran']	2022-12-07	null	null	null	null	['image-dehazing']	['computer-vision']	[ 6.11872435e-01 4.80573744e-01 3.48299265e-01 -3.38920504e-02 7.79620954e-04 1.83141798e-01 5.17342508e-01 -6.66569531e-01 -3.14154506e-01 5.80143452e-01 3.01580995e-01 -3.45659792e-01 -1.91826671e-01 -7.98555374e-01 -5.76612532e-01 -1.12339497e+00 -3.14331353e-01 -3.70033771e-01 5.43975890e-01 -4.65889066e-01 2.50079811e-01 1.90023273e-01 -1.45248234e+00 1.42848015e-01 5.73744774e-01 1.38858402e+00 4.29745883e-01 9.45415318e-01 5.83926797e-01 1.22335815e+00 -6.33227289e-01 -6.82069957e-01 5.88816524e-01 -5.24761260e-01 -5.12316644e-01 3.52982283e-02 4.71558392e-01 -1.10615957e+00 -1.02550960e+00 9.57163990e-01 7.04801500e-01 1.20979927e-01 4.44389880e-01 -1.11214674e+00 -6.42212272e-01 3.00478637e-01 -1.51975378e-01 2.09571853e-01 -1.62536606e-01 2.17635110e-01 6.24855638e-01 -6.88794672e-01 4.80769694e-01 1.01381338e+00 5.41075826e-01 4.88227457e-01 -1.03527570e+00 -6.87844753e-01 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c0741372-224b-4d5f-9a87-bb7fca68ea06	a-mcts-search-with-theoretical-guarantee	null	null	https://openreview.net/forum?id=SJloA0EYDr	https://openreview.net/pdf?id=SJloA0EYDr	A⋆MCTS: SEARCH WITH THEORETICAL GUARANTEE USING POLICY AND VALUE FUNCTIONS	Combined with policy and value neural networks, Monte Carlos Tree Search (MCTS) is a critical component of the recent success of AI agents in learning to play board games like Chess and Go (Silver et al., 2017). However, the theoretical foundations of MCTS with policy and value networks remains open. Inspired by MCTS, we propose A⋆MCTS, a novel search algorithm that uses both the policy and value predictors to guide search and enjoys theoretical guarantees. Specifically, assuming that value and policy networks give reasonably accurate signals of the values of each state and action, the sample complexity (number of calls to the value network) to estimate the value of the current state, as well as the optimal one-step action to take from the current state, can be bounded. We apply our theoretical framework to different models for the noise distribution of the policy and value network as well as the distribution of rewards, and show that for these general models, the sample complexity is polynomial in D, where D is the depth of the search tree. Empirically, our method outperforms MCTS in these models.	['Lexing Ying', 'Yuandong Tian', 'Xian Wu']	2019-09-25	null	null	null	null	['board-games']	['playing-games']	[-1.70507491e-01 1.20715171e-01 -6.08107269e-01 3.95253971e-02 -5.93666375e-01 -8.52407753e-01 5.15990257e-01 1.53667450e-01 -7.97067940e-01 1.00265896e+00 -9.42566991e-02 -5.02713740e-01 -3.27716529e-01 -9.84624267e-01 -7.37201452e-01 -8.63296330e-01 -3.22256148e-01 7.49525547e-01 4.59876418e-01 -1.01129122e-01 3.33592355e-01 2.94952482e-01 -1.14644790e+00 -2.98849463e-01 5.83631575e-01 1.30573559e+00 5.55781573e-02 1.03629708e+00 2.67960399e-01 1.27793944e+00 -5.78493834e-01 -2.05623001e-01 2.90695161e-01 -5.12220740e-01 -7.39293575e-01 -1.25904903e-01 -3.00741762e-01 -8.06757092e-01 -6.06221437e-01 1.28187966e+00 4.15066153e-01 4.56381410e-01 3.84845436e-01 -1.17795193e+00 -6.35031424e-03 1.07547128e+00 -3.65026921e-01 3.26563150e-01 -1.57153025e-01 5.85155785e-01 1.16270578e+00 2.25888923e-01 5.35668135e-01 1.12839985e+00 2.15380609e-01 7.39086211e-01 -1.11877406e+00 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3f4cabd7-3f99-40ba-b8db-ed0fe4c7a403	magnetic-resonance-fingerprinting-1	1909.06395	null	https://arxiv.org/abs/1909.06395v1	https://arxiv.org/pdf/1909.06395v1.pdf	Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks	Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previous methods based on CNNs, RNN models yield significantly improved results using in-vivo data.	['Andreas Maier', 'Gregor Körzdörfer', 'Heiko Meyer', 'Franziska Schirrmacher', 'Elisabeth Hoppe', 'Mathias Nittka', 'Florian Thamm', 'Christopher Syben', 'Josef Pfeuffer']	2019-09-13	null	null	null	null	['magnetic-resonance-fingerprinting']	['medical']	[ 4.86556023e-01 -8.23703483e-02 -1.13460243e-01 -3.65026385e-01 -6.66377008e-01 -5.29211946e-02 3.00078779e-01 -1.82538286e-01 -5.19684911e-01 4.80458081e-01 2.70405244e-02 1.39004961e-02 -5.37085056e-01 -5.12247205e-01 -4.63520020e-01 -6.68372929e-01 -3.20521891e-01 2.27922305e-01 2.57407784e-01 -1.49277225e-01 1.23160228e-01 8.48873973e-01 -1.27755094e+00 3.99403572e-01 4.13693547e-01 9.93488312e-01 3.41308057e-01 2.29472414e-01 -1.25194654e-01 8.75267267e-01 -5.21295190e-01 1.73344433e-01 1.09307036e-01 -3.94549549e-01 -7.04723954e-01 -5.20788848e-01 4.15504903e-01 -2.24560603e-01 -8.69008601e-01 1.10868502e+00 7.27061570e-01 1.76669300e-01 2.83654064e-01 -9.20414329e-01 -4.19463903e-01 8.73238385e-01 -1.54970989e-01 5.13405561e-01 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