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/deep-architectures-for-neural-machine	Deep Architectures for Neural Machine Translation	1707.07631	http://arxiv.org/abs/1707.07631v1	http://arxiv.org/pdf/1707.07631v1.pdf	https://github.com/Avmb/deep-nmt-architectures	true	true	false	none
https://paperswithcode.com/paper/generalization-and-equilibrium-in-generative	Generalization and Equilibrium in Generative Adversarial Nets (GANs)	1703.00573	http://arxiv.org/abs/1703.00573v5	http://arxiv.org/pdf/1703.00573v5.pdf	https://github.com/PrincetonML/MIX-plus-GANs	true	true	true	none
https://paperswithcode.com/paper/neural-factorization-machines-for-sparse	Neural Factorization Machines for Sparse Predictive Analytics	1708.05027	http://arxiv.org/abs/1708.05027v1	http://arxiv.org/pdf/1708.05027v1.pdf	https://github.com/hexiangnan/neural_factorization_machine	true	true	false	tf
https://paperswithcode.com/paper/how-intelligent-are-convolutional-neural	How intelligent are convolutional neural networks?	1709.06126	http://arxiv.org/abs/1709.06126v2	http://arxiv.org/pdf/1709.06126v2.pdf	https://github.com/zhennany/synthetic	true	true	true	none
https://paperswithcode.com/paper/learning-a-rotation-invariant-detector-with	Learning a Rotation Invariant Detector with Rotatable Bounding Box	1711.09405	http://arxiv.org/abs/1711.09405v1	http://arxiv.org/pdf/1711.09405v1.pdf	https://github.com/liulei01/DRBox	true	true	true	none
https://paperswithcode.com/paper/denoising-adversarial-autoencoders	Denoising Adversarial Autoencoders	1703.01220	http://arxiv.org/abs/1703.01220v4	http://arxiv.org/pdf/1703.01220v4.pdf	https://github.com/ToniCreswell/DAAE_	true	true	false	none
https://paperswithcode.com/paper/texture-synthesis-with-recurrent-variational	Texture Synthesis with Recurrent Variational Auto-Encoder	1712.08838	http://arxiv.org/abs/1712.08838v1	http://arxiv.org/pdf/1712.08838v1.pdf	https://github.com/MoustafaMeshry/draw	true	true	false	tf
https://paperswithcode.com/paper/toward-controlled-generation-of-text	Toward Controlled Generation of Text	1703.00955	http://arxiv.org/abs/1703.00955v4	http://arxiv.org/pdf/1703.00955v4.pdf	https://github.com/asyml/texar	true	true	false	tf
https://paperswithcode.com/paper/deep-uq-learning-deep-neural-network	Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification	1802.00850	http://arxiv.org/abs/1802.00850v1	http://arxiv.org/pdf/1802.00850v1.pdf	https://github.com/rohitkt10/deep-uq-paper	true	true	false	tf
https://paperswithcode.com/paper/tensorflow-quantum-a-software-framework-for	TensorFlow Quantum: A Software Framework for Quantum Machine Learning	2003.02989	https://arxiv.org/abs/2003.02989v2	https://arxiv.org/pdf/2003.02989v2.pdf	https://github.com/tensorflow/quantum	true	true	true	tf
https://paperswithcode.com/paper/action-segmentation-with-joint-self	Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation	2003.02824	https://arxiv.org/abs/2003.02824v3	https://arxiv.org/pdf/2003.02824v3.pdf	https://github.com/cmhungsteve/SSTDA	true	true	true	pytorch
https://paperswithcode.com/paper/dancing-to-music	Dancing to Music	1911.02001	https://arxiv.org/abs/1911.02001v1	https://arxiv.org/pdf/1911.02001v1.pdf	https://github.com/NVlabs/Dance2Music	true	true	true	pytorch
https://paperswithcode.com/paper/deep-reinforcement-learning-control-of	Deep Reinforcement Learning Control of Quantum Cartpoles	1910.09200	https://arxiv.org/abs/1910.09200v4	https://arxiv.org/pdf/1910.09200v4.pdf	https://github.com/Z-T-WANG/DeepReinforcementLearningControlOfQuantumCartpoles	true	true	true	pytorch
https://paperswithcode.com/paper/nonlinear-classifiers-for-ranking-problems	Nonlinear classifiers for ranking problems based on kernelized SVM	2002.11436	https://arxiv.org/abs/2002.11436v2	https://arxiv.org/pdf/2002.11436v2.pdf	https://github.com/VaclavMacha/ClassificationOnTop_new.jl	true	true	true	none
https://paperswithcode.com/paper/weakly-and-semi-supervised-panoptic	Weakly- and Semi-Supervised Panoptic Segmentation	1808.03575	http://arxiv.org/abs/1808.03575v3	http://arxiv.org/pdf/1808.03575v3.pdf	https://github.com/qizhuli/Weakly-Supervised-Panoptic-Segmentation	true	true	true	none
https://paperswithcode.com/paper/multi-task-self-supervised-learning-for-1	Multi-task self-supervised learning for Robust Speech Recognition	2001.09239	https://arxiv.org/abs/2001.09239v2	https://arxiv.org/pdf/2001.09239v2.pdf	https://github.com/santi-pdp/pase	true	true	true	pytorch
https://paperswithcode.com/paper/a-model-to-search-for-synthesizable-molecules	A Model to Search for Synthesizable Molecules	1906.05221	https://arxiv.org/abs/1906.05221v2	https://arxiv.org/pdf/1906.05221v2.pdf	https://github.com/john-bradshaw/molecule-chef	true	true	true	pytorch
https://paperswithcode.com/paper/adversarial-policy-gradient-for-deep-learning	Adversarial Policy Gradient for Deep Learning Image Augmentation	1909.04108	https://arxiv.org/abs/1909.04108v1	https://arxiv.org/pdf/1909.04108v1.pdf	https://github.com/victorychain/Adversarial-Policy-Gradient-Augmentation	true	true	true	pytorch
https://paperswithcode.com/paper/improved-regularization-of-convolutional	Improved Regularization of Convolutional Neural Networks with Cutout	1708.04552	http://arxiv.org/abs/1708.04552v2	http://arxiv.org/pdf/1708.04552v2.pdf	https://github.com/uoguelph-mlrg/Cutout	true	true	true	pytorch
https://paperswithcode.com/paper/real-time-vision-based-depth-reconstruction	Real-time Vision-based Depth Reconstruction with NVidia Jetson	1907.07210	https://arxiv.org/abs/1907.07210v1	https://arxiv.org/pdf/1907.07210v1.pdf	https://github.com/CnnDepth/tx2_fcnn_node	true	true	true	tf
https://paperswithcode.com/paper/a-kernel-perspective-for-regularizing-deep	A Kernel Perspective for Regularizing Deep Neural Networks	1810.00363	https://arxiv.org/abs/1810.00363v4	https://arxiv.org/pdf/1810.00363v4.pdf	https://github.com/albietz/kernel_reg	true	true	true	pytorch
https://paperswithcode.com/paper/query-guided-end-to-end-person-search	Query-guided End-to-End Person Search	1905.01203	https://arxiv.org/abs/1905.01203v1	https://arxiv.org/pdf/1905.01203v1.pdf	https://github.com/munjalbharti/Query-guided-End-to-End-Person-Search	true	true	true	none
https://paperswithcode.com/paper/r2cnn-multi-dimensional-attention-based	SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects	1811.07126	https://arxiv.org/abs/1811.07126v4	https://arxiv.org/pdf/1811.07126v4.pdf	https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow	true	true	true	tf
https://paperswithcode.com/paper/towards-query-efficient-black-box-attacks-an	Towards Query Efficient Black-box Attacks: An Input-free Perspective	1809.02918	http://arxiv.org/abs/1809.02918v1	http://arxiv.org/pdf/1809.02918v1.pdf	https://github.com/yalidu/input-free-attack	true	true	true	tf
https://paperswithcode.com/paper/texar-a-modularized-versatile-and-extensible-1	Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation	1809.00794	https://arxiv.org/abs/1809.00794v2	https://arxiv.org/pdf/1809.00794v2.pdf	https://github.com/asyml/texar	true	true	true	tf
https://paperswithcode.com/paper/automatic-program-synthesis-of-long-programs	Automatic Program Synthesis of Long Programs with a Learned Garbage Collector	1809.04682	http://arxiv.org/abs/1809.04682v2	http://arxiv.org/pdf/1809.04682v2.pdf	https://github.com/amitz25/PCCoder	true	true	true	pytorch
https://paperswithcode.com/paper/acquisition-of-localization-confidence-for	Acquisition of Localization Confidence for Accurate Object Detection	1807.11590	http://arxiv.org/abs/1807.11590v1	http://arxiv.org/pdf/1807.11590v1.pdf	https://github.com/vacancy/PreciseRoIPooling	true	true	true	pytorch
https://paperswithcode.com/paper/a-repetition-code-of-15-qubits	A repetition code of 15 qubits	1709.00990	https://arxiv.org/abs/1709.00990v3	https://arxiv.org/pdf/1709.00990v3.pdf	https://github.com/decodoku/repetition_code	true	true	true	none
https://paperswithcode.com/paper/eddy-saturation-of-the-southern-ocean-a	Eddy saturation of the Southern Ocean: a baroclinic versus barotropic perspective	1906.08442	https://arxiv.org/abs/1906.08442v3	https://arxiv.org/pdf/1906.08442v3.pdf	https://github.com/navidcy/EddySaturation-MOM6	true	true	false	none
https://paperswithcode.com/paper/topic-modeling-with-wasserstein-autoencoders-1	Topic Modeling with Wasserstein Autoencoders	1907.12374	https://arxiv.org/abs/1907.12374v2	https://arxiv.org/pdf/1907.12374v2.pdf	https://github.com/awslabs/w-lda	true	true	false	mxnet
https://paperswithcode.com/paper/neural-duplicate-question-detection-without-1	Neural Duplicate Question Detection without Labeled Training Data	1911.05594	https://arxiv.org/abs/1911.05594v2	https://arxiv.org/pdf/1911.05594v2.pdf	https://github.com/UKPLab/emnlp2019-duplicate_question_detection	true	true	false	none
https://paperswithcode.com/paper/neural-attribution-for-semantic-bug	Neural Attribution for Semantic Bug-Localization in Student Programs	null	http://papers.nips.cc/paper/9358-neural-attribution-for-semantic-bug-localization-in-student-programs	http://papers.nips.cc/paper/9358-neural-attribution-for-semantic-bug-localization-in-student-programs.pdf	https://bitbucket.org/iiscseal/nbl	true	true	false	none
https://paperswithcode.com/paper/understanding-contrastive-representation-1	Understanding Contrastive Representation Learning through Geometry on the Hypersphere	null	https://proceedings.icml.cc/static/paper_files/icml/2020/5503-Paper.pdf	https://proceedings.icml.cc/static/paper_files/icml/2020/5503-Paper.pdf	https://github.com/SsnL/align_uniform	true	true	false	pytorch
https://paperswithcode.com/paper/vision-based-dynamic-offside-line-marker-for	Vision Based Dynamic Offside Line Marker for Soccer Games	1804.06438	http://arxiv.org/abs/1804.06438v1	http://arxiv.org/pdf/1804.06438v1.pdf	https://github.com/surajkra/Offside_Tracker_EECS504	true	true	true	none
https://paperswithcode.com/paper/colors-in-context-a-pragmatic-neural-model	Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding	1703.10186	http://arxiv.org/abs/1703.10186v2	http://arxiv.org/pdf/1703.10186v2.pdf	https://github.com/futurulus/colors-in-context	false	false	true	none
https://paperswithcode.com/paper/draw-a-recurrent-neural-network-for-image	DRAW: A Recurrent Neural Network For Image Generation	1502.04623	http://arxiv.org/abs/1502.04623v2	http://arxiv.org/pdf/1502.04623v2.pdf	https://github.com/MoustafaMeshry/draw	false	false	true	tf
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/LuminosoInsight/conceptnet-vector-ensemble	false	false	true	none
https://paperswithcode.com/paper/conceptnet-at-semeval-2017-task-2-extending	ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge	1704.03560	http://arxiv.org/abs/1704.03560v2	http://arxiv.org/pdf/1704.03560v2.pdf	https://github.com/LuminosoInsight/conceptnet-vector-ensemble	false	false	true	none
https://paperswithcode.com/paper/incorporating-copying-mechanism-in-sequence	Incorporating Copying Mechanism in Sequence-to-Sequence Learning	1603.06393	http://arxiv.org/abs/1603.06393v3	http://arxiv.org/pdf/1603.06393v3.pdf	https://github.com/majumderb/sanskrit-ocr	false	false	true	tf
https://paperswithcode.com/paper/transition-based-dependency-parsing-with-2	Transition-Based Dependency Parsing with Stack Long Short-Term Memory	1505.08075	http://arxiv.org/abs/1505.08075v1	http://arxiv.org/pdf/1505.08075v1.pdf	https://github.com/mstrise/dep2label	false	false	true	pytorch
https://paperswithcode.com/paper/deconvolutional-paragraph-representation	Deconvolutional Paragraph Representation Learning	1708.04729	http://arxiv.org/abs/1708.04729v3	http://arxiv.org/pdf/1708.04729v3.pdf	https://github.com/tuvuumass/SCoPE	false	false	true	tf
https://paperswithcode.com/paper/a-hierarchical-neural-autoencoder-for	A Hierarchical Neural Autoencoder for Paragraphs and Documents	1506.01057	http://arxiv.org/abs/1506.01057v2	http://arxiv.org/pdf/1506.01057v2.pdf	https://github.com/tuvuumass/SCoPE	false	false	true	tf
https://paperswithcode.com/paper/character-level-convolutional-networks-for	Character-level Convolutional Networks for Text Classification	1509.01626	http://arxiv.org/abs/1509.01626v3	http://arxiv.org/pdf/1509.01626v3.pdf	https://github.com/tuvuumass/SCoPE	false	false	true	tf
https://paperswithcode.com/paper/191202288	Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning	1912.02288	https://arxiv.org/abs/1912.02288v2	https://arxiv.org/pdf/1912.02288v2.pdf	https://github.com/facebookresearch/Hanabi_SPARTA	false	false	true	pytorch
https://paperswithcode.com/paper/large-scale-visual-relationship-understanding	Large-Scale Visual Relationship Understanding	1804.10660	https://arxiv.org/abs/1804.10660v4	https://arxiv.org/pdf/1804.10660v4.pdf	https://github.com/facebookresearch/Large-Scale-VRD	false	false	true	pytorch
https://paperswithcode.com/paper/solving-nonlinear-and-high-dimensional	Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning	1811.08782	https://arxiv.org/abs/1811.08782v1	https://arxiv.org/pdf/1811.08782v1.pdf	https://github.com/alialaradi/DeepGalerkinMethod	false	false	true	tf
https://paperswithcode.com/paper/paired-open-ended-trailblazer-poet-endlessly	Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions	1901.01753	http://arxiv.org/abs/1901.01753v3	http://arxiv.org/pdf/1901.01753v3.pdf	https://github.com/uber-research/poet	false	false	true	none
https://paperswithcode.com/paper/deepercut-a-deeper-stronger-and-faster-multi	DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model	1605.03170	http://arxiv.org/abs/1605.03170v3	http://arxiv.org/pdf/1605.03170v3.pdf	https://github.com/gyaansastra/DeepLab	false	false	true	tf
https://paperswithcode.com/paper/deep-learning-tools-for-the-measurement-of	Deep learning tools for the measurement of animal behavior in neuroscience	1909.13868	https://arxiv.org/abs/1909.13868v2	https://arxiv.org/pdf/1909.13868v2.pdf	https://github.com/gyaansastra/DeepLab	false	false	true	tf
https://paperswithcode.com/paper/squeeze-excite-guided-few-shot-segmentation	'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images	1902.01314	https://arxiv.org/abs/1902.01314v2	https://arxiv.org/pdf/1902.01314v2.pdf	https://github.com/CSCYQJ/LOCATION-SENSITIVE-LOCAL-PROTOTYPE-NETWORK	false	false	true	pytorch
https://paperswithcode.com/paper/large-covariance-estimation-by-thresholding	Large Covariance Estimation by Thresholding Principal Orthogonal Complements	1201.0175	https://arxiv.org/abs/1201.0175v2	https://arxiv.org/pdf/1201.0175v2.pdf	https://github.com/brucewuquant/POET	false	false	true	none
https://paperswithcode.com/paper/model-of-spin-liquids-with-and-without-time	Model of spin liquids with and without time-reversal symmetry	1810.09858	https://arxiv.org/abs/1810.09858v1	https://arxiv.org/pdf/1810.09858v1.pdf	https://github.com/gonghour/DMRG_for_spin-ladder_systems	false	false	true	none
https://paperswithcode.com/paper/pannuke-dataset-extension-insights-and	PanNuke Dataset Extension, Insights and Baselines	2003.10778	https://arxiv.org/abs/2003.10778v7	https://arxiv.org/pdf/2003.10778v7.pdf	https://github.com/aaparna/UNet-Image-Segmentation	false	false	true	none
https://paperswithcode.com/paper/safe-by-design-control-for-euler-lagrange	Safe-by-Design Control for Euler-Lagrange Systems	2009.03767	https://arxiv.org/abs/2009.03767v2	https://arxiv.org/pdf/2009.03767v2.pdf	https://github.com/shawcortez/safe-control-euler-lagrange	true	true	true	none
https://paperswithcode.com/paper/deep-learning-with-differential-privacy	Deep Learning with Differential Privacy	1607.00133	http://arxiv.org/abs/1607.00133v2	http://arxiv.org/pdf/1607.00133v2.pdf	https://github.com/zzzer1019/FL_DP	false	false	true	tf
https://paperswithcode.com/paper/learning-to-pivot-with-adversarial-networks	Learning to Pivot with Adversarial Networks	1611.01046	http://arxiv.org/abs/1611.01046v3	http://arxiv.org/pdf/1611.01046v3.pdf	https://github.com/faroukmokhtar/GradProject	false	false	true	none
https://paperswithcode.com/paper/search-for-supersymmetry-in-events-with-one	Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at sqrt(s) = 13 TeV	1609.09386	https://arxiv.org/abs/1609.09386v2	https://arxiv.org/pdf/1609.09386v2.pdf	https://github.com/faroukmokhtar/GradProject	false	false	true	none
https://paperswithcode.com/paper/autoencoder-by-forest	AutoEncoder by Forest	1709.09018	http://arxiv.org/abs/1709.09018v1	http://arxiv.org/pdf/1709.09018v1.pdf	https://github.com/AntoinePassemiers/Encoder-Forest	false	false	true	none
https://paperswithcode.com/paper/semantic-segmentation-of-underwater-imagery	Semantic Segmentation of Underwater Imagery: Dataset and Benchmark	2004.01241	https://arxiv.org/abs/2004.01241v3	https://arxiv.org/pdf/2004.01241v3.pdf	https://github.com/xahidbuffon/SUIM	false	false	true	none
https://paperswithcode.com/paper/traditional-and-accelerated-gradient-descent	Traditional and accelerated gradient descent for neural architecture search	2006.15218	https://arxiv.org/abs/2006.15218v3	https://arxiv.org/pdf/2006.15218v3.pdf	https://github.com/bibliotecadebabel/EvAI	false	false	true	pytorch
https://paperswithcode.com/paper/segnet-a-deep-convolutional-encoder-decoder-1	SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling	1505.07293	http://arxiv.org/abs/1505.07293v1	http://arxiv.org/pdf/1505.07293v1.pdf	https://github.com/xahidbuffon/SUIM	false	false	true	none
https://paperswithcode.com/paper/rethinking-atrous-convolution-for-semantic	Rethinking Atrous Convolution for Semantic Image Segmentation	1706.05587	http://arxiv.org/abs/1706.05587v3	http://arxiv.org/pdf/1706.05587v3.pdf	https://github.com/xahidbuffon/SUIM	false	false	true	none
https://paperswithcode.com/paper/repvgg-making-vgg-style-convnets-great-again	RepVGG: Making VGG-style ConvNets Great Again	2101.03697	https://arxiv.org/abs/2101.03697v3	https://arxiv.org/pdf/2101.03697v3.pdf	https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/repvgg.py	false	false	false	mindspore
https://paperswithcode.com/paper/mastering-2048-with-delayed-temporal	Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping	1604.05085	http://arxiv.org/abs/1604.05085v3	http://arxiv.org/pdf/1604.05085v3.pdf	https://github.com/abachurin/2048	false	false	true	tf
https://paperswithcode.com/paper/blockwise-self-attention-for-long-document	Blockwise Self-Attention for Long Document Understanding	1911.02972	https://arxiv.org/abs/1911.02972v2	https://arxiv.org/pdf/1911.02972v2.pdf	https://github.com/xptree/BlockBERT	true	true	false	none
https://paperswithcode.com/paper/automatic-discrete-differentiation-and-its	Deep Energy-Based Modeling of Discrete-Time Physics	1905.08604	https://arxiv.org/abs/1905.08604v3	https://arxiv.org/pdf/1905.08604v3.pdf	https://github.com/tksmatsubara/discrete-autograd	true	true	true	pytorch
https://paperswithcode.com/paper/understanding-and-improving-interpolation-in	Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer	1807.07543	http://arxiv.org/abs/1807.07543v2	http://arxiv.org/pdf/1807.07543v2.pdf	https://github.com/baohq1595/aae-experiment	false	false	true	tf
https://paperswithcode.com/paper/adversarial-autoencoders	Adversarial Autoencoders	1511.05644	http://arxiv.org/abs/1511.05644v2	http://arxiv.org/pdf/1511.05644v2.pdf	https://github.com/baohq1595/aae-experiment	false	false	true	tf
https://paperswithcode.com/paper/yolov3-an-incremental-improvement	YOLOv3: An Incremental Improvement	1804.02767	http://arxiv.org/abs/1804.02767v1	http://arxiv.org/pdf/1804.02767v1.pdf	https://github.com/sadicLiu/yolov3	false	false	true	pytorch
https://paperswithcode.com/paper/high-quality-monocular-depth-estimation-via	High Quality Monocular Depth Estimation via Transfer Learning	1812.11941	http://arxiv.org/abs/1812.11941v2	http://arxiv.org/pdf/1812.11941v2.pdf	https://github.com/Intoxillectual/Monocular-Depth-Estimation-using-DenseNet169	false	false	true	tf
https://paperswithcode.com/paper/deep-complex-networks	Deep Complex Networks	1705.09792	http://arxiv.org/abs/1705.09792v4	http://arxiv.org/pdf/1705.09792v4.pdf	https://github.com/ypeleg/komplex	false	false	true	tf
https://paperswithcode.com/paper/on-complex-valued-convolutional-neural	On Complex Valued Convolutional Neural Networks	1602.09046	http://arxiv.org/abs/1602.09046v1	http://arxiv.org/pdf/1602.09046v1.pdf	https://github.com/ypeleg/komplex	false	false	true	tf
https://paperswithcode.com/paper/complex-valued-neural-networks-with-non	Complex-valued Neural Networks with Non-parametric Activation Functions	1802.08026	http://arxiv.org/abs/1802.08026v1	http://arxiv.org/pdf/1802.08026v1.pdf	https://github.com/ypeleg/komplex	false	false	true	tf
https://paperswithcode.com/paper/one-shot-visual-imitation-learning-via-meta	One-Shot Visual Imitation Learning via Meta-Learning	1709.04905	http://arxiv.org/abs/1709.04905v1	http://arxiv.org/pdf/1709.04905v1.pdf	https://github.com/ErickRosete/MAML-Imitation-Learning	false	false	true	pytorch
https://paperswithcode.com/paper/physical-layer-encryption-using-a-vernam	Physical Layer Encryption using a Vernam Cipher	1910.08262	https://arxiv.org/abs/1910.08262v1	https://arxiv.org/pdf/1910.08262v1.pdf	https://github.com/ymirsky/VPSC-py	false	false	true	none
https://paperswithcode.com/paper/wide-deep-learning-for-recommender-systems	Wide & Deep Learning for Recommender Systems	1606.07792	http://arxiv.org/abs/1606.07792v1	http://arxiv.org/pdf/1606.07792v1.pdf	https://github.com/sandeepnair2812/Deep-Learning-Based-Search-and-Recommendation-System	false	false	true	tf
https://paperswithcode.com/paper/deepfm-a-factorization-machine-based-neural	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	1703.04247	http://arxiv.org/abs/1703.04247v1	http://arxiv.org/pdf/1703.04247v1.pdf	https://github.com/sandeepnair2812/Deep-Learning-Based-Search-and-Recommendation-System	false	false	true	tf
https://paperswithcode.com/paper/unsupervised-representation-learning-with-1	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	1511.06434	http://arxiv.org/abs/1511.06434v2	http://arxiv.org/pdf/1511.06434v2.pdf	https://github.com/Sezoir/DCGAN-Dog-Generator	false	false	true	tf
https://paperswithcode.com/paper/oriented-point-sampling-for-plane-detection	Oriented Point Sampling for Plane Detection in Unorganized Point Clouds	1905.02553	https://arxiv.org/abs/1905.02553v1	https://arxiv.org/pdf/1905.02553v1.pdf	https://github.com/bsun7/Oriented-Point-Sampling	false	false	true	none
https://paperswithcode.com/paper/multistep-inverse-is-not-all-you-need	Multistep Inverse Is Not All You Need	2403.11940	https://arxiv.org/abs/2403.11940v2	https://arxiv.org/pdf/2403.11940v2.pdf	https://github.com/midi-lab/acdf	true	true	false	pytorch
https://paperswithcode.com/paper/160803828	Prutor: A System for Tutoring CS1 and Collecting Student Programs for Analysis	1608.03828	http://arxiv.org/abs/1608.03828v1	http://arxiv.org/pdf/1608.03828v1.pdf	https://github.com/umairzahmed/seet2020	false	false	true	none
https://paperswithcode.com/paper/pypsa-eur-an-open-optimisation-model-of-the	PyPSA-Eur: An Open Optimisation Model of the European Transmission System	1806.01613	http://arxiv.org/abs/1806.01613v1	http://arxiv.org/pdf/1806.01613v1.pdf	https://github.com/pz-max/energyworld	false	false	true	none
https://paperswithcode.com/paper/language-agnostic-bert-sentence-embedding	Language-agnostic BERT Sentence Embedding	2007.01852	https://arxiv.org/abs/2007.01852v2	https://arxiv.org/pdf/2007.01852v2.pdf	https://github.com/bojone/labse	false	false	true	tf
https://paperswithcode.com/paper/vulnerability-of-deep-reinforcement-learning	Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks	1701.04143	http://arxiv.org/abs/1701.04143v1	http://arxiv.org/pdf/1701.04143v1.pdf	https://github.com/coderatwork7/attack	false	false	true	tf
https://paperswithcode.com/paper/model-free-bounds-for-multi-asset-options	Model-free bounds for multi-asset options using option-implied information and their exact computation	2006.14288	https://arxiv.org/abs/2006.14288v3	https://arxiv.org/pdf/2006.14288v3.pdf	https://github.com/qikunxiang/ModelFreePriceBounds	true	true	true	none
https://paperswithcode.com/paper/detecting-persuasive-atypicality-by-modeling	Detecting Persuasive Atypicality by Modeling Contextual Compatibility	null	http://openaccess.thecvf.com//content/ICCV2021/html/Guo_Detecting_Persuasive_Atypicality_by_Modeling_Contextual_Compatibility_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_Detecting_Persuasive_Atypicality_by_Modeling_Contextual_Compatibility_ICCV_2021_paper.pdf	https://github.com/meiqiguo/iccv2021-atypicalitydetection	true	true	false	pytorch
https://paperswithcode.com/paper/ubermag-towards-more-effective-micromagnetic	Ubermag: Towards more effective micromagnetic workflows	2105.08355	https://arxiv.org/abs/2105.08355v1	https://arxiv.org/pdf/2105.08355v1.pdf	https://github.com/marijanbeg/2021-paper-ubermag	true	true	false	none
https://paperswithcode.com/paper/conditional-image-synthesis-with-auxiliary	Conditional Image Synthesis With Auxiliary Classifier GANs	1610.09585	http://arxiv.org/abs/1610.09585v4	http://arxiv.org/pdf/1610.09585v4.pdf	https://github.com/kushalpatil1997/text_to_image_synthesis	false	false	true	tf
https://paperswithcode.com/paper/skip-thought-vectors	Skip-Thought Vectors	1506.06726	http://arxiv.org/abs/1506.06726v1	http://arxiv.org/pdf/1506.06726v1.pdf	https://github.com/kushalpatil1997/text_to_image_synthesis	false	false	true	tf
https://paperswithcode.com/paper/mask-shadowgan-learning-to-remove-shadows	Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data	1903.10683	https://arxiv.org/abs/1903.10683v3	https://arxiv.org/pdf/1903.10683v3.pdf	https://github.com/mducducd/ghost-free-shadow-removal	false	false	true	tf
https://paperswithcode.com/paper/tac-gan-text-conditioned-auxiliary-classifier	TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network	1703.06412	http://arxiv.org/abs/1703.06412v2	http://arxiv.org/pdf/1703.06412v2.pdf	https://github.com/kushalpatil1997/text_to_image_synthesis	false	false	true	tf
https://paperswithcode.com/paper/towards-ghost-free-shadow-removal-via-dual	Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN	1911.08718	https://arxiv.org/abs/1911.08718v2	https://arxiv.org/pdf/1911.08718v2.pdf	https://github.com/mducducd/ghost-free-shadow-removal	false	false	true	tf
https://paperswithcode.com/paper/single-image-reflection-separation-with	Single Image Reflection Separation with Perceptual Losses	1806.05376	http://arxiv.org/abs/1806.05376v1	http://arxiv.org/pdf/1806.05376v1.pdf	https://github.com/mducducd/ghost-free-shadow-removal	false	false	true	tf
https://paperswithcode.com/paper/estimating-or-propagating-gradients-through-1	Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation	1308.3432	http://arxiv.org/abs/1308.3432v1	http://arxiv.org/pdf/1308.3432v1.pdf	https://github.com/georgeretsi/SparsityLoss	false	false	true	pytorch
https://paperswithcode.com/paper/bias-correction-of-learned-generative-models	Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting	1906.09531	https://arxiv.org/abs/1906.09531v2	https://arxiv.org/pdf/1906.09531v2.pdf	https://github.com/kevtran23/autoregressive_bias_correction	false	false	true	pytorch
https://paperswithcode.com/paper/pano-avqa-grounded-audio-visual-question	Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg Videos	null	http://openaccess.thecvf.com//content/ICCV2021/html/Yun_Pano-AVQA_Grounded_Audio-Visual_Question_Answering_on_360deg_Videos_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Yun_Pano-AVQA_Grounded_Audio-Visual_Question_Answering_on_360deg_Videos_ICCV_2021_paper.pdf	https://github.com/hs-yn/panoavqa	true	true	false	pytorch
https://paperswithcode.com/paper/deep-evidential-regression	Deep Evidential Regression	1910.02600	https://arxiv.org/abs/1910.02600v2	https://arxiv.org/pdf/1910.02600v2.pdf	https://github.com/deebuls/deep_evidential_regression_loss_pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/automatic-fault-detection-for-deep-learning	Automatic Fault Detection for Deep Learning Programs Using Graph Transformations	2105.08095	https://arxiv.org/abs/2105.08095v2	https://arxiv.org/pdf/2105.08095v2.pdf	https://github.com/neuralint/neuralint	true	true	false	tf
https://paperswithcode.com/paper/eegnet-a-compact-convolutional-network-for	EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces	1611.08024	http://arxiv.org/abs/1611.08024v4	http://arxiv.org/pdf/1611.08024v4.pdf	https://github.com/adwaykanhere/FYP	false	false	true	pytorch
https://paperswithcode.com/paper/lewis-levenshtein-editing-for-unsupervised	LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer	2105.08206	https://arxiv.org/abs/2105.08206v1	https://arxiv.org/pdf/2105.08206v1.pdf	https://github.com/machelreid/lewis	true	true	false	pytorch

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.