Split (1)

train · 300k rows

paper_url stringlengths 36 81	paper_title stringlengths 1 242 ⌀	paper_arxiv_id stringlengths 9 16 ⌀	paper_url_abs stringlengths 18 314	paper_url_pdf stringlengths 21 935 ⌀	repo_url stringlengths 26 200	is_official bool 2 classes	mentioned_in_paper bool 2 classes	mentioned_in_github bool 2 classes	framework stringclasses 9 values
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

Subsets and Splits

Framework Repo Connectivity Analysis

Reveals the number of official and unofficial repositories and papers associated with different frameworks, highlighting the most connected ones.

Deduplicated Paper-Code Links

This query provides a detailed and organized list of repositories linked to single papers, highlighting official status and mention sources, which is useful for understanding the relationship between papers and their corresponding repositories.

Paper Repo Counts & Distribution

Provides detailed statistics on the distribution of papers across different numbers of repositories, highlighting the percentage of papers with multiple repositories.

Quantum Papers with Code Links

Lists quantum-related papers with their titles, arXiv IDs, frameworks, and code repository links, providing a valuable resource for researchers interested in quantum computing.

Financial Stock Price Prediction

Finds papers related to stock prices, financial markets, and predictions, providing a focused subset for further analysis.

SQL Console for pwc-archive/links-between-paper-and-code

Retrieves specific details about a single paper by its arXiv ID, providing limited insight into the dataset.

Search for YOLO Links

Retrieves a limited set of records related to YOLO, providing basic information about papers and repositories but without deeper analysis.

Prompt Optimization and Personalization

Retrieves a limited set of papers with titles containing specific keywords related to prompt optimization and personalization, providing basic filtering of the dataset.