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https://paperswithcode.com/paper/double-path-networks-for-sequence-to-sequence
|
Double Path Networks for Sequence to Sequence Learning
|
1806.04856
|
http://arxiv.org/abs/1806.04856v2
|
http://arxiv.org/pdf/1806.04856v2.pdf
|
https://github.com/StillKeepTry/Transformer-PyTorch
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/it-takes-four-to-tango-multiagent-self-play
|
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
| null |
https://openreview.net/forum?id=q4tZR1Y-UIs
|
https://openreview.net/pdf?id=q4tZR1Y-UIs
|
https://github.com/yuqingd/cusp
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/on-the-effectiveness-of-discretizing
|
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers
|
1701.07114
|
http://arxiv.org/abs/1701.07114v1
|
http://arxiv.org/pdf/1701.07114v1.pdf
|
https://github.com/vedic-partap/Discretization
| false | false | true |
none
|
https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q
|
Deep Reinforcement Learning with Double Q-learning
|
1509.06461
|
http://arxiv.org/abs/1509.06461v3
|
http://arxiv.org/pdf/1509.06461v3.pdf
|
https://github.com/wmol4/Pytorch_DDQN_Unity_Navigation
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/xnmt-the-extensible-neural-machine
|
XNMT: The eXtensible Neural Machine Translation Toolkit
|
1803.00188
|
http://arxiv.org/abs/1803.00188v1
|
http://arxiv.org/pdf/1803.00188v1.pdf
|
https://github.com/neulab/xnmt
| true | true | false |
none
|
https://paperswithcode.com/paper/generalised-dice-overlap-as-a-deep-learning
|
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
|
1707.03237
|
http://arxiv.org/abs/1707.03237v3
|
http://arxiv.org/pdf/1707.03237v3.pdf
|
https://github.com/IAmSuyogJadhav/Brainy
| false | false | true |
none
|
https://paperswithcode.com/paper/uncertainty-sampling-is-preconditioned
|
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
|
1812.01815
|
http://arxiv.org/abs/1812.01815v1
|
http://arxiv.org/pdf/1812.01815v1.pdf
|
https://worksheets.codalab.org/worksheets/0xf8dfe5bcc1dc408fb54b3cc15a5abce8
| true | false | false |
none
|
https://paperswithcode.com/paper/understanding-black-box-predictions-via
|
Understanding Black-box Predictions via Influence Functions
|
1703.04730
|
https://arxiv.org/abs/1703.04730v3
|
https://arxiv.org/pdf/1703.04730v3.pdf
|
https://worksheets.codalab.org/worksheets/0x2b314dc3536b482dbba02783a24719fd
| true | false | false |
none
|
https://paperswithcode.com/paper/learning-sparse-2d-temporal-adjacent-networks
|
Learning Sparse 2D Temporal Adjacent Networks for Temporal Action Localization
|
1912.03612
|
https://arxiv.org/abs/1912.03612v1
|
https://arxiv.org/pdf/1912.03612v1.pdf
|
https://github.com/researchmm/2D-TAN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/hyper-path-based-representation-learning-for
|
Hyper-Path-Based Representation Learning for Hyper-Networks
|
1908.09152
|
https://arxiv.org/abs/1908.09152v2
|
https://arxiv.org/pdf/1908.09152v2.pdf
|
https://github.com/HKUST-KnowComp/HPHG
| true | true | true |
none
|
https://paperswithcode.com/paper/spike-train-level-backpropagation-for
|
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
|
1908.06378
|
https://arxiv.org/abs/1908.06378v3
|
https://arxiv.org/pdf/1908.06378v3.pdf
|
https://github.com/stonezwr/ST-RSBP
| true | true | true |
none
|
https://paperswithcode.com/paper/expand-and-compress-exploring-tuning
|
Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
|
2410.12593
|
https://arxiv.org/abs/2410.12593v1
|
https://arxiv.org/pdf/2410.12593v1.pdf
|
https://github.com/Onedean/EAC
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/lending-orientation-to-neural-networks-for
|
Lending Orientation to Neural Networks for Cross-view Geo-localization
|
1903.12351
|
http://arxiv.org/abs/1903.12351v1
|
http://arxiv.org/pdf/1903.12351v1.pdf
|
https://github.com/Liumouliu/OriCNN
| true | true | false |
tf
|
https://paperswithcode.com/paper/mida-multiple-imputation-using-denoising
|
MIDA: Multiple Imputation using Denoising Autoencoders
|
1705.02737
|
http://arxiv.org/abs/1705.02737v3
|
http://arxiv.org/pdf/1705.02737v3.pdf
|
https://github.com/HarryK24/MIDA-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/encoder-decoder-with-atrous-separable
|
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
|
1802.02611
|
http://arxiv.org/abs/1802.02611v3
|
http://arxiv.org/pdf/1802.02611v3.pdf
|
https://github.com/nolanliou/PeopleSegmentationDemo
| false | false | true |
tf
|
https://paperswithcode.com/paper/mobilenetv2-inverted-residuals-and-linear
|
MobileNetV2: Inverted Residuals and Linear Bottlenecks
|
1801.04381
|
http://arxiv.org/abs/1801.04381v4
|
http://arxiv.org/pdf/1801.04381v4.pdf
|
https://github.com/nolanliou/PeopleSegmentationDemo
| false | false | true |
tf
|
https://paperswithcode.com/paper/structural-estimation-of-behavioral
|
Structural Estimation of Behavioral Heterogeneity
|
1802.03735
|
http://arxiv.org/abs/1802.03735v2
|
http://arxiv.org/pdf/1802.03735v2.pdf
|
https://github.com/zhentaoshi/behavioral_heterogeneity
| false | false | true |
none
|
https://paperswithcode.com/paper/a-reduction-of-imitation-learning-and
|
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
|
1011.0686
|
http://arxiv.org/abs/1011.0686v3
|
http://arxiv.org/pdf/1011.0686v3.pdf
|
https://github.com/Refefer/Dagger
| false | false | true |
none
|
https://paperswithcode.com/paper/mastering-chess-and-shogi-by-self-play-with-a
|
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
|
1712.01815
|
http://arxiv.org/abs/1712.01815v1
|
http://arxiv.org/pdf/1712.01815v1.pdf
|
https://github.com/intenseG/BSK
| false | false | true |
tf
|
https://paperswithcode.com/paper/densely-connected-convolutional-networks
|
Densely Connected Convolutional Networks
|
1608.06993
|
http://arxiv.org/abs/1608.06993v5
|
http://arxiv.org/pdf/1608.06993v5.pdf
|
https://github.com/wangbinglin1995/tianchi
| false | false | true |
tf
|
https://paperswithcode.com/paper/taming-pre-trained-language-models-with-n
|
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation
| null |
https://aclanthology.org/2021.acl-long.259
|
https://aclanthology.org/2021.acl-long.259.pdf
|
https://github.com/shizhediao/t-dna
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/region-of-attraction-for-power-systems-using
|
Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study
|
1906.03590
|
https://arxiv.org/abs/1906.03590v1
|
https://arxiv.org/pdf/1906.03590v1.pdf
|
https://github.com/Chaocas/ROA-for-Power-Systems
| true | true | false |
none
|
https://paperswithcode.com/paper/learning-what-and-where-to-transfer
|
Learning What and Where to Transfer
|
1905.05901
|
https://arxiv.org/abs/1905.05901v1
|
https://arxiv.org/pdf/1905.05901v1.pdf
|
https://github.com/jindongwang/transferlearning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/topologically-driven-methods-for-construction
|
Topologically Driven Methods for Construction Of Multi-Edge Type (Multigraph with nodes puncturing) Quasi-Cyclic Low-density Parity-check Codes for Wireless Channel, WDM Long-Haul and Archival Holographic Memory
|
2011.14753
|
https://arxiv.org/abs/2011.14753v3
|
https://arxiv.org/pdf/2011.14753v3.pdf
|
https://github.com/Lcrypto/Protograph-Sieving-Method-for-Construction-MET-LDPC-codes
| true | false | false |
none
|
https://paperswithcode.com/paper/face-super-resolution-through-wasserstein
|
Face Super-Resolution Through Wasserstein GANs
|
1705.02438
|
http://arxiv.org/abs/1705.02438v1
|
http://arxiv.org/pdf/1705.02438v1.pdf
|
https://github.com/MandyZChen/srez
| true | true | true |
tf
|
https://paperswithcode.com/paper/realistic-evaluation-of-deep-semi-supervised
|
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
|
1804.09170
|
https://arxiv.org/abs/1804.09170v4
|
https://arxiv.org/pdf/1804.09170v4.pdf
|
https://github.com/siit-vtt/semi-supervised-learning-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/generalized-low-rank-models
|
Generalized Low Rank Models
|
1410.0342
|
http://arxiv.org/abs/1410.0342v4
|
http://arxiv.org/pdf/1410.0342v4.pdf
|
https://github.com/madeleineudell/LowRankModels.jl
| true | true | false |
none
|
https://paperswithcode.com/paper/predicting-pairwise-relations-with-neural
|
Predicting Pairwise Relations with Neural Similarity Encoders
|
1702.01824
|
http://arxiv.org/abs/1702.01824v2
|
http://arxiv.org/pdf/1702.01824v2.pdf
|
https://github.com/cod3licious/simec
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/s3fd-single-shot-scale-invariant-face
|
S$^3$FD: Single Shot Scale-invariant Face Detector
|
1708.05237
|
http://arxiv.org/abs/1708.05237v3
|
http://arxiv.org/pdf/1708.05237v3.pdf
|
https://github.com/LeeRel1991/SFD
| false | false | true |
none
|
https://paperswithcode.com/paper/the-tatoeba-translation-challenge-realistic
|
The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT
|
2010.06354
|
https://arxiv.org/abs/2010.06354v1
|
https://arxiv.org/pdf/2010.06354v1.pdf
|
https://github.com/Helsinki-NLP/Tatoeba-Challenge
| true | true | true |
none
|
https://paperswithcode.com/paper/learning-the-joint-representation-of
|
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
|
1803.04837
|
http://arxiv.org/abs/1803.04837v4
|
http://arxiv.org/pdf/1803.04837v4.pdf
|
https://github.com/pkusjh/HELSTM
| true | true | true |
none
|
https://paperswithcode.com/paper/city-wide-analysis-of-electronic-health
|
City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions
|
1803.03571
|
https://arxiv.org/abs/1803.03571v4
|
https://arxiv.org/pdf/1803.03571v4.pdf
|
https://github.com/rionbr/DDIBlumenau
| true | true | true |
none
|
https://paperswithcode.com/paper/number-parsing-at-a-gigabyte-per-second
|
Number Parsing at a Gigabyte per Second
|
2101.11408
|
https://arxiv.org/abs/2101.11408v9
|
https://arxiv.org/pdf/2101.11408v9.pdf
|
https://github.com/eddelbuettel/rcppfastfloat
| false | false | true |
none
|
https://paperswithcode.com/paper/real-time-monocular-depth-estimation-using
|
Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style Transfer
| null |
http://openaccess.thecvf.com/content_cvpr_2018/html/Atapour-Abarghouei_Real-Time_Monocular_Depth_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Atapour-Abarghouei_Real-Time_Monocular_Depth_CVPR_2018_paper.pdf
|
https://github.com/atapour/monocularDepth-Inference
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/science-with-the-cherenkov-telescope-array
|
Science with the Cherenkov Telescope Array
|
1709.07997
|
http://arxiv.org/abs/1709.07997v2
|
http://arxiv.org/pdf/1709.07997v2.pdf
|
https://github.com/UofA-HEAG/CTA-Oz-School
| false | false | true |
none
|
https://paperswithcode.com/paper/self-attention-generative-adversarial
|
Self-Attention Generative Adversarial Networks
|
1805.08318
|
https://arxiv.org/abs/1805.08318v2
|
https://arxiv.org/pdf/1805.08318v2.pdf
|
https://github.com/sdoria/SimpleSelfAttention
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/predicting-fluid-intelligence-of-children
|
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
|
1904.07387
|
https://arxiv.org/abs/1904.07387v3
|
https://arxiv.org/pdf/1904.07387v3.pdf
|
https://github.com/pykao/ABCD-MICCAI2019
| true | true | true |
none
|
https://paperswithcode.com/paper/a-generic-inverted-index-framework-for
|
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
|
1603.08390
|
http://arxiv.org/abs/1603.08390v3
|
http://arxiv.org/pdf/1603.08390v3.pdf
|
https://github.com/SeSaMe-NUS/genie
| true | true | true |
none
|
https://paperswithcode.com/paper/tunability-importance-of-hyperparameters-of
|
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
|
1802.09596
|
http://arxiv.org/abs/1802.09596v3
|
http://arxiv.org/pdf/1802.09596v3.pdf
|
https://github.com/PhilippPro/tunability
| true | true | false |
none
|
https://paperswithcode.com/paper/using-random-effects-to-account-for-high
|
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks
| null |
http://proceedings.neurips.cc/paper/2021/hash/d35b05a832e2bb91f110d54e34e2da79-Abstract.html
|
http://proceedings.neurips.cc/paper/2021/file/d35b05a832e2bb91f110d54e34e2da79-Paper.pdf
|
https://github.com/gsimchoni/lmmnn
| true | true | false |
none
|
https://paperswithcode.com/paper/dsa-more-efficient-budgeted-pruning-via
|
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
|
2004.02164
|
https://arxiv.org/abs/2004.02164v5
|
https://arxiv.org/pdf/2004.02164v5.pdf
|
https://github.com/walkerning/differentiable-sparsity-allocation
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional
|
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
|
1810.04805
|
https://arxiv.org/abs/1810.04805v2
|
https://arxiv.org/pdf/1810.04805v2.pdf
|
https://github.com/lovedavidsilva/bert_old_version
| false | false | true |
tf
|
https://paperswithcode.com/paper/towards-hardware-aware-tractable-learning-of
|
Towards Hardware-Aware Tractable Learning of Probabilistic Models
| null |
http://papers.nips.cc/paper/9525-towards-hardware-aware-tractable-learning-of-probabilistic-models
|
http://papers.nips.cc/paper/9525-towards-hardware-aware-tractable-learning-of-probabilistic-models.pdf
|
https://github.com/laurago894/HwAwareProb
| true | true | false |
none
|
https://paperswithcode.com/paper/detecting-hate-speech-in-multi-modal-memes
|
Detecting Hate Speech in Multi-modal Memes
|
2012.14891
|
https://arxiv.org/abs/2012.14891v1
|
https://arxiv.org/pdf/2012.14891v1.pdf
|
https://github.com/Abhishek0697/Detection-of-Hate-Speech-in-Multimodal-Memes
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/image-to-image-translation-with-conditional
|
Image-to-Image Translation with Conditional Adversarial Networks
|
1611.07004
|
http://arxiv.org/abs/1611.07004v3
|
http://arxiv.org/pdf/1611.07004v3.pdf
|
https://github.com/Vious/LBAM_Pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/skipnet-learning-dynamic-routing-in
|
SkipNet: Learning Dynamic Routing in Convolutional Networks
|
1711.09485
|
http://arxiv.org/abs/1711.09485v2
|
http://arxiv.org/pdf/1711.09485v2.pdf
|
https://github.com/geekJZY/arcticnet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-probabilistic-u-net-for-segmentation-of
|
A Probabilistic U-Net for Segmentation of Ambiguous Images
|
1806.05034
|
http://arxiv.org/abs/1806.05034v4
|
http://arxiv.org/pdf/1806.05034v4.pdf
|
https://github.com/stefanknegt/probabilistic_unet_pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/copy-and-paste-a-simple-but-effective
|
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
|
1906.06086
|
https://arxiv.org/abs/1906.06086v2
|
https://arxiv.org/pdf/1906.06086v2.pdf
|
https://github.com/ttbrunner/blackbox_starting_points
| true | true | true |
tf
|
https://paperswithcode.com/paper/integrating-and-querying-similar-tables-from
|
Integrating and querying similar tables from PDF documents using deep learning
|
1901.04672
|
https://arxiv.org/abs/1901.04672v1
|
https://arxiv.org/pdf/1901.04672v1.pdf
|
https://github.com/dhavalpotdar/Bounding-box-Classifier
| false | false | true |
none
|
https://paperswithcode.com/paper/bayesian-sparsification-methods-for-deep
|
Bayesian Sparsification Methods for Deep Complex-valued Networks
|
2003.11413
|
https://arxiv.org/abs/2003.11413v2
|
https://arxiv.org/pdf/2003.11413v2.pdf
|
https://github.com/ivannz/complex_paper
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/computing-exact-guarantees-for-differential
|
Computing Tight Differential Privacy Guarantees Using FFT
|
1906.03049
|
https://arxiv.org/abs/1906.03049v2
|
https://arxiv.org/pdf/1906.03049v2.pdf
|
https://github.com/DPBayes/PLD-Accountant
| true | true | true |
none
|
https://paperswithcode.com/paper/the-collective-knowledge-project-making-ml
|
The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
|
2006.07161
|
https://arxiv.org/abs/2006.07161v2
|
https://arxiv.org/pdf/2006.07161v2.pdf
|
https://github.com/ctuning/cbench
| true | true | true |
tf
|
https://paperswithcode.com/paper/stochastic-kinetic-treatment-of-protein
|
Stochastic kinetic treatment of protein aggregation and the effects of macromolecular crowding
|
2102.01569
|
https://arxiv.org/abs/2102.01569v1
|
https://arxiv.org/pdf/2102.01569v1.pdf
|
https://github.com/jljorgenson18/popsim
| true | true | false |
none
|
https://paperswithcode.com/paper/towards-improving-solution-dominance-with
|
Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining
|
1910.00505
|
https://arxiv.org/abs/1910.00505v1
|
https://arxiv.org/pdf/1910.00505v1.pdf
|
https://github.com/stacs-cp/ModRef2019-Dominance
| true | true | false |
none
|
https://paperswithcode.com/paper/restoration-of-non-rigidly-distorted
|
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image Representations
|
1908.01940
|
https://arxiv.org/abs/1908.01940v1
|
https://arxiv.org/pdf/1908.01940v1.pdf
|
https://github.com/jeringeo/CompressiveFlows
| true | true | true |
none
|
https://paperswithcode.com/paper/training-deep-autoencoders-for-collaborative
|
Training Deep AutoEncoders for Collaborative Filtering
|
1708.01715
|
http://arxiv.org/abs/1708.01715v3
|
http://arxiv.org/pdf/1708.01715v3.pdf
|
https://github.com/NVIDIA/DeepRecommender
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/boosting-scene-character-recognition-by
|
Boosting Scene Character Recognition by Learning Canonical Forms of Glyphs
|
1907.05577
|
https://arxiv.org/abs/1907.05577v2
|
https://arxiv.org/pdf/1907.05577v2.pdf
|
https://github.com/Actasidiot/CGRN
| false | false | true |
tf
|
https://paperswithcode.com/paper/darts-differentiable-architecture-search
|
DARTS: Differentiable Architecture Search
|
1806.09055
|
http://arxiv.org/abs/1806.09055v2
|
http://arxiv.org/pdf/1806.09055v2.pdf
|
https://github.com/abcp4/MyDarts
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/accurate-large-minibatch-sgd-training
|
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
|
1706.02677
|
http://arxiv.org/abs/1706.02677v2
|
http://arxiv.org/pdf/1706.02677v2.pdf
|
https://github.com/abcp4/MyDarts
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-factored-generalized-additive-model-for
|
A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room
|
1907.12596
|
https://arxiv.org/abs/1907.12596v1
|
https://arxiv.org/pdf/1907.12596v1.pdf
|
https://github.com/nostringattached/FGAM
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/joint-discriminative-and-generative-learning
|
Joint Discriminative and Generative Learning for Person Re-identification
|
1904.07223
|
https://arxiv.org/abs/1904.07223v3
|
https://arxiv.org/pdf/1904.07223v3.pdf
|
https://github.com/NVlabs/DG-Net
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/conceptnet-55-an-open-multilingual-graph-of
|
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
|
1612.03975
|
http://arxiv.org/abs/1612.03975v2
|
http://arxiv.org/pdf/1612.03975v2.pdf
|
https://github.com/shayanray/ApplyingCommonSense
| false | false | true |
none
|
https://paperswithcode.com/paper/training-deep-autoencoders-for-collaborative
|
Training Deep AutoEncoders for Collaborative Filtering
|
1708.01715
|
http://arxiv.org/abs/1708.01715v3
|
http://arxiv.org/pdf/1708.01715v3.pdf
|
https://github.com/yrbahn/Deep-AutoEncoders-for-Collaborative-Filtering
| false | false | true |
tf
|
https://paperswithcode.com/paper/decoupled-deep-neural-network-for-semi
|
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
|
1506.04924
|
http://arxiv.org/abs/1506.04924v2
|
http://arxiv.org/pdf/1506.04924v2.pdf
|
https://github.com/GoNgXiAoPeNg1/caffeBVLCplus
| false | false | true |
none
|
https://paperswithcode.com/paper/exploration-by-random-network-distillation
|
Exploration by Random Network Distillation
|
1810.12894
|
http://arxiv.org/abs/1810.12894v1
|
http://arxiv.org/pdf/1810.12894v1.pdf
|
https://github.com/kngwyu/intrinsic-rewards
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/rethinking-graph-autoencoder-models-for
|
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering
|
2107.08562
|
https://arxiv.org/abs/2107.08562v3
|
https://arxiv.org/pdf/2107.08562v3.pdf
|
https://github.com/nairouz/R-GAE
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-finnish-news-corpus-for-named-entity
|
A Finnish News Corpus for Named Entity Recognition
|
1908.04212
|
https://arxiv.org/abs/1908.04212v1
|
https://arxiv.org/pdf/1908.04212v1.pdf
|
https://github.com/mpsilfve/finer-data
| false | false | true |
none
|
https://paperswithcode.com/paper/unsupervised-efficient-and-semantic-expertise
|
Unsupervised, Efficient and Semantic Expertise Retrieval
|
1608.06651
|
http://arxiv.org/abs/1608.06651v2
|
http://arxiv.org/pdf/1608.06651v2.pdf
|
https://github.com/cvangysel/SERT
| true | true | true |
none
|
https://paperswithcode.com/paper/efficient-neural-architecture-search-via-1
|
Efficient Neural Architecture Search via Parameter Sharing
|
1802.03268
|
http://arxiv.org/abs/1802.03268v2
|
http://arxiv.org/pdf/1802.03268v2.pdf
|
https://github.com/MengTianjian/enas-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/improving-unsupervised-defect-segmentation-by
|
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
|
1807.02011
|
http://arxiv.org/abs/1807.02011v3
|
http://arxiv.org/pdf/1807.02011v3.pdf
|
https://github.com/daxiaHuang/Unsupervised_Defect_Segmentation
| false | false | true |
tf
|
https://paperswithcode.com/paper/attention-is-all-you-need
|
Attention Is All You Need
|
1706.03762
|
https://arxiv.org/abs/1706.03762v7
|
https://arxiv.org/pdf/1706.03762v7.pdf
|
https://github.com/tbmoon/LANL_Earthquake_Prediction
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/the-impact-of-modelling-choices-on-modelling
|
The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain)
|
2009.12625
|
https://arxiv.org/abs/2009.12625v1
|
https://arxiv.org/pdf/2009.12625v1.pdf
|
https://github.com/albrizre/COVID_Catalonia
| false | false | true |
none
|
https://paperswithcode.com/paper/a-quantum-approximate-optimization-algorithm-1
|
A Quantum Approximate Optimization Algorithm
|
1411.4028
|
http://arxiv.org/abs/1411.4028v1
|
http://arxiv.org/pdf/1411.4028v1.pdf
|
https://github.com/Lucaman99/Cirq-Quantum-Computing
| false | false | true |
tf
|
https://paperswithcode.com/paper/u-gat-it-unsupervised-generative-attentional
|
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
|
1907.10830
|
https://arxiv.org/abs/1907.10830v4
|
https://arxiv.org/pdf/1907.10830v4.pdf
|
https://github.com/wkcn/UGATIT-mxnet
| false | false | true |
mxnet
|
https://paperswithcode.com/paper/label-noise-reduction-in-entity-typing-by
|
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
|
1602.05307
|
http://arxiv.org/abs/1602.05307v1
|
http://arxiv.org/pdf/1602.05307v1.pdf
|
https://github.com/shanzhenren/AFET
| false | false | true |
none
|
https://paperswithcode.com/paper/quantifying-the-benefits-of-carbon-aware
|
On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloud
|
2306.06502
|
https://arxiv.org/abs/2306.06502v2
|
https://arxiv.org/pdf/2306.06502v2.pdf
|
https://github.com/umassos/decarbonization-potential
| true | true | false |
none
|
https://paperswithcode.com/paper/190503381
|
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
|
1905.03381
|
https://arxiv.org/abs/1905.03381v1
|
https://arxiv.org/pdf/1905.03381v1.pdf
|
https://github.com/zhangjiong724/autoassist-exp
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/hide-and-seek-privacy-challenge
|
Hide-and-Seek Privacy Challenge
|
2007.12087
|
https://arxiv.org/abs/2007.12087v2
|
https://arxiv.org/pdf/2007.12087v2.pdf
|
https://github.com/vanderschaarlab/hide-and-seek-submissions
| false | false | true |
none
|
https://paperswithcode.com/paper/real-time-and-interactive-tools-for-vocal
|
Real-time and interactive tools for vocal training based on an analytic signal with a cosine series envelope
|
1909.03650
|
https://arxiv.org/abs/1909.03650v1
|
https://arxiv.org/pdf/1909.03650v1.pdf
|
https://github.com/HidekiKawahara/voiceRTFB
| false | false | true |
none
|
https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
|
MixConv: Mixed Depthwise Convolutional Kernels
|
1907.09595
|
https://arxiv.org/abs/1907.09595v3
|
https://arxiv.org/pdf/1907.09595v3.pdf
|
https://github.com/zsef123/MixNet-PyTorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-ros-multi-ontology-references-services-owl
|
A ROS multi-ontology references services: OWL reasoners and application prototyping issues
|
1706.10151
|
https://arxiv.org/abs/1706.10151v2
|
https://arxiv.org/pdf/1706.10151v2.pdf
|
https://github.com/EmaroLab/injected_armor_pkgs
| true | true | true |
none
|
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object
|
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
|
1506.01497
|
http://arxiv.org/abs/1506.01497v3
|
http://arxiv.org/pdf/1506.01497v3.pdf
|
https://github.com/lincaiming/py-faster-rcnn-update
| false | false | true |
none
|
https://paperswithcode.com/paper/noisy-as-clean-learning-unsupervised
|
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
|
1906.06878
|
https://arxiv.org/abs/1906.06878v4
|
https://arxiv.org/pdf/1906.06878v4.pdf
|
https://github.com/csjunxu/Noisy-As-Clean
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/four-things-everyone-should-know-to-improve
|
Four Things Everyone Should Know to Improve Batch Normalization
|
1906.03548
|
https://arxiv.org/abs/1906.03548v2
|
https://arxiv.org/pdf/1906.03548v2.pdf
|
https://github.com/nixx14/Ghost-BatchNormalisation-
| false | false | true |
none
|
https://paperswithcode.com/paper/gated-graph-sequence-neural-networks
|
Gated Graph Sequence Neural Networks
|
1511.05493
|
http://arxiv.org/abs/1511.05493v4
|
http://arxiv.org/pdf/1511.05493v4.pdf
|
https://github.com/entslscheia/GGNN_Reasoning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/edge-labeling-based-directed-gated-graph
|
Edge-Labeling based Directed Gated Graph Network for Few-shot Learning
|
2101.11299
|
https://arxiv.org/abs/2101.11299v1
|
https://arxiv.org/pdf/2101.11299v1.pdf
|
https://github.com/zpx16900/DGGN
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/190409331
|
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction
|
1904.09331
|
https://arxiv.org/abs/1904.09331v2
|
https://arxiv.org/pdf/1904.09331v2.pdf
|
https://github.com/INK-USC/shifted-label-distribution
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/semi-discrete-optimization-through-semi
|
Semi-discrete optimization through semi-discrete optimal transport: a framework for neural architecture search
|
2006.15221
|
https://arxiv.org/abs/2006.15221v2
|
https://arxiv.org/pdf/2006.15221v2.pdf
|
https://github.com/bibliotecadebabel/EvAI
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/weakly-supervised-cell-instance-segmentation
|
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
|
1911.13077
|
https://arxiv.org/abs/1911.13077v1
|
https://arxiv.org/pdf/1911.13077v1.pdf
|
https://github.com/naivete5656/WSISPDR
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-common-semantic-space-for-monolingual-and
|
A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddings
|
2001.06381
|
https://arxiv.org/abs/2001.06381v2
|
https://arxiv.org/pdf/2001.06381v2.pdf
|
https://github.com/ikergarcia1996/MVM-Embeddings
| true | true | false |
tf
|
https://paperswithcode.com/paper/privacy-preserving-deep-visual-recognition-an
|
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
|
1906.05675
|
https://arxiv.org/abs/1906.05675v6
|
https://arxiv.org/pdf/1906.05675v6.pdf
|
https://github.com/TAMU-VITA/Privacy-AdversarialLearning
| false | false | true |
tf
|
https://paperswithcode.com/paper/learning-to-generate-time-lapse-videos-using
|
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
|
1709.07592
|
http://arxiv.org/abs/1709.07592v3
|
http://arxiv.org/pdf/1709.07592v3.pdf
|
https://github.com/CompVis/image2video-synthesis-using-cINNs
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/simple-online-and-realtime-tracking-with-a
|
Simple Online and Realtime Tracking with a Deep Association Metric
|
1703.07402
|
http://arxiv.org/abs/1703.07402v1
|
http://arxiv.org/pdf/1703.07402v1.pdf
|
https://github.com/MacherLabs/deep_sort
| false | false | true |
tf
|
https://paperswithcode.com/paper/attention-is-all-you-need
|
Attention Is All You Need
|
1706.03762
|
https://arxiv.org/abs/1706.03762v7
|
https://arxiv.org/pdf/1706.03762v7.pdf
|
https://github.com/enhuiz/transformer-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/aspect-level-sentiment-classification-with-1
|
Aspect Level Sentiment Classification with Deep Memory Network
|
1605.08900
|
http://arxiv.org/abs/1605.08900v2
|
http://arxiv.org/pdf/1605.08900v2.pdf
|
https://github.com/ridakadri14/AspectBasedSentimentAnalysis
| false | false | true |
tf
|
https://paperswithcode.com/paper/g-tad-sub-graph-localization-for-temporal
|
G-TAD: Sub-Graph Localization for Temporal Action Detection
|
1911.11462
|
https://arxiv.org/abs/1911.11462v2
|
https://arxiv.org/pdf/1911.11462v2.pdf
|
https://github.com/812618101/TAL-Demo
| false | false | true |
none
|
https://paperswithcode.com/paper/deep-concept-wise-temporal-convolutional
|
Deep Concept-wise Temporal Convolutional Networks for Action Localization
|
1908.09442
|
https://arxiv.org/abs/1908.09442v1
|
https://arxiv.org/pdf/1908.09442v1.pdf
|
https://github.com/812618101/TAL-Demo
| false | false | true |
none
|
https://paperswithcode.com/paper/towards-cooperative-data-rate-prediction-for
|
Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks
|
2001.09452
|
https://arxiv.org/abs/2001.09452v1
|
https://arxiv.org/pdf/2001.09452v1.pdf
|
https://github.com/falkenber9/falcon
| true | true | true |
none
|
https://paperswithcode.com/paper/normalized-wasserstein-distance-for-mixture
|
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
|
1902.00415
|
https://arxiv.org/abs/1902.00415v2
|
https://arxiv.org/pdf/1902.00415v2.pdf
|
https://github.com/yogeshbalaji/Normalized-Wasserstein
| true | true | true |
tf
|
https://paperswithcode.com/paper/deepar-probabilistic-forecasting-with
|
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
|
1704.04110
|
http://arxiv.org/abs/1704.04110v3
|
http://arxiv.org/pdf/1704.04110v3.pdf
|
https://github.com/Timbasa/Sample_GluonTS
| false | false | true |
none
|
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