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/towards-high-performance-video-object	Towards High Performance Video Object Detection for Mobiles	1804.05830	http://arxiv.org/abs/1804.05830v1	http://arxiv.org/pdf/1804.05830v1.pdf	https://github.com/stanlee321/LightFlow-Keras	false	false	true	tf
https://paperswithcode.com/paper/accurate-3d-localization-for-mav-swarms-by	Accurate 3D Localization for MAV Swarms by UWB and IMU Fusion	1807.10913	http://arxiv.org/abs/1807.10913v1	http://arxiv.org/pdf/1807.10913v1.pdf	https://github.com/lijx10/uwb-localization	true	true	true	none
https://paperswithcode.com/paper/photo-realistic-single-image-super-resolution	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network	1609.04802	http://arxiv.org/abs/1609.04802v5	http://arxiv.org/pdf/1609.04802v5.pdf	https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks	false	false	true	none
https://paperswithcode.com/paper/structural-learning-of-probabilistic	Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption	2107.12130	https://arxiv.org/abs/2107.12130v1	https://arxiv.org/pdf/2107.12130v1.pdf	https://github.com/IDSIA-papers/2021-TPM	true	true	false	none
https://paperswithcode.com/paper/classifying-conversation-in-digital	Classifying Conversation in Digital Communication	1801.10527	http://arxiv.org/abs/1801.10527v1	http://arxiv.org/pdf/1801.10527v1.pdf	https://github.com/empiricalstateofmind/eventgraphs	true	false	true	none
https://paperswithcode.com/paper/real-time-2d-multi-person-pose-estimation-on	Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose	1811.12004	http://arxiv.org/abs/1811.12004v1	http://arxiv.org/pdf/1811.12004v1.pdf	https://github.com/murdockhou/lightweight_openpose	false	false	true	tf
https://paperswithcode.com/paper/virtual-cnn-branching-efficient-feature	Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification	1803.05872	http://arxiv.org/abs/1803.05872v1	http://arxiv.org/pdf/1803.05872v1.pdf	https://github.com/agongt408/vbranch	false	false	true	tf
https://paperswithcode.com/paper/in-defense-of-the-triplet-loss-for-person-re	In Defense of the Triplet Loss for Person Re-Identification	1703.07737	http://arxiv.org/abs/1703.07737v4	http://arxiv.org/pdf/1703.07737v4.pdf	https://github.com/agongt408/vbranch	false	false	true	tf
https://paperswithcode.com/paper/pristi-a-conditional-diffusion-framework-for	PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation	2302.09746	https://arxiv.org/abs/2302.09746v1	https://arxiv.org/pdf/2302.09746v1.pdf	https://github.com/lmzzml/pristi	true	true	false	pytorch
https://paperswithcode.com/paper/mixed-effect-composite-rnn-gp-a-personalized	Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare	1806.01551	https://arxiv.org/abs/1806.01551v3	https://arxiv.org/pdf/1806.01551v3.pdf	https://github.com/OpenXAIProject/Mixed-Effect-Composite-RNN-Gaussian-Process	false	false	true	tf
https://paperswithcode.com/paper/adversarial-attacks-on-time-series	Adversarial Attacks on Time Series	1902.10755	http://arxiv.org/abs/1902.10755v2	http://arxiv.org/pdf/1902.10755v2.pdf	https://github.com/titu1994/Adversarial-Attacks-Time-Series	false	false	true	tf
https://paperswithcode.com/paper/prototypical-representation-learning-for-1	Prototypical Representation Learning for Relation Extraction	2103.11647	https://arxiv.org/abs/2103.11647v1	https://arxiv.org/pdf/2103.11647v1.pdf	https://github.com/Alibaba-NLP/ProtoRE	true	true	false	pytorch
https://paperswithcode.com/paper/sparse-multiway-decomposition-for-analysis	Sparse multiway decomposition for analysis and modeling of diffusion imaging and tractography	1505.07170	http://arxiv.org/abs/1505.07170v1	http://arxiv.org/pdf/1505.07170v1.pdf	https://github.com/brainlife/app-life	false	false	true	none
https://paperswithcode.com/paper/attanet-attention-augmented-network-for-fast	AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing	2103.05930	https://arxiv.org/abs/2103.05930v1	https://arxiv.org/pdf/2103.05930v1.pdf	https://github.com/songqi-github/AttaNet	false	false	true	pytorch
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector	SSD: Single Shot MultiBox Detector	1512.02325	http://arxiv.org/abs/1512.02325v5	http://arxiv.org/pdf/1512.02325v5.pdf	https://github.com/nsom/ssd	false	false	true	pytorch
https://paperswithcode.com/paper/dissent-sentence-representation-learning-from	DisSent: Sentence Representation Learning from Explicit Discourse Relations	1710.04334	https://arxiv.org/abs/1710.04334v4	https://arxiv.org/pdf/1710.04334v4.pdf	https://github.com/facebookresearch/InferSent	false	false	true	pytorch
https://paperswithcode.com/paper/pixel-wise-attentional-gating-for	Pixel-wise Attentional Gating for Parsimonious Pixel Labeling	1805.01556	http://arxiv.org/abs/1805.01556v2	http://arxiv.org/pdf/1805.01556v2.pdf	https://github.com/aimerykong/Pixel-Attentional-Gating	true	true	true	none
https://paperswithcode.com/paper/chexnet-radiologist-level-pneumonia-detection	CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning	1711.05225	http://arxiv.org/abs/1711.05225v3	http://arxiv.org/pdf/1711.05225v3.pdf	https://github.com/Azure/AzureChestXRay	false	false	true	pytorch
https://paperswithcode.com/paper/random-erasing-data-augmentation	Random Erasing Data Augmentation	1708.04896	http://arxiv.org/abs/1708.04896v2	http://arxiv.org/pdf/1708.04896v2.pdf	https://github.com/NVlabs/DG-Net	false	false	false	pytorch
https://paperswithcode.com/paper/mnemonic-descent-method-a-recurrent-process-1	Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment	null	https://www.ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf	https://www.ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf	https://github.com/trigeorgis/mdm	false	false	false	tf
https://paperswithcode.com/paper/catboost-unbiased-boosting-with-categorical	CatBoost: unbiased boosting with categorical features	1706.09516	http://arxiv.org/abs/1706.09516v5	http://arxiv.org/pdf/1706.09516v5.pdf	https://github.com/yumoh/catboost_iter	false	false	true	none
https://paperswithcode.com/paper/unity-a-general-platform-for-intelligent	Unity: A General Platform for Intelligent Agents	1809.02627	https://arxiv.org/abs/1809.02627v2	https://arxiv.org/pdf/1809.02627v2.pdf	https://github.com/Henreich/ML-Pong	false	false	true	tf
https://paperswithcode.com/paper/if-you-like-it-gan-it-probabilistic	If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN	2005.01181	https://arxiv.org/abs/2005.01181v1	https://arxiv.org/pdf/2005.01181v1.pdf	https://github.com/flaviagiammarino/probcast-tensorflow	false	false	true	tf
https://paperswithcode.com/paper/reading-between-the-lines-can-llms-identify	Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?	2502.09636	https://arxiv.org/abs/2502.09636v2	https://arxiv.org/pdf/2502.09636v2.pdf	https://github.com/sougata-ub/reading_between_lines	true	true	false	none
https://paperswithcode.com/paper/semi-supervised-learning-with-deep-generative-1	Semi-Supervised Learning with Deep Generative Models	1406.5298	http://arxiv.org/abs/1406.5298v2	http://arxiv.org/pdf/1406.5298v2.pdf	https://github.com/enalisnick/stick-breaking_dgms	false	false	true	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/neshitov/UNet	false	false	true	pytorch
https://paperswithcode.com/paper/crowdsourcing-gaze-data-collection	Crowdsourcing Gaze Data Collection	1204.3367	https://arxiv.org/abs/1204.3367v1	https://arxiv.org/pdf/1204.3367v1.pdf	https://github.com/turkeyes/codecharts	false	false	true	none
https://paperswithcode.com/paper/yolo9000-better-faster-stronger	YOLO9000: Better, Faster, Stronger	1612.08242	http://arxiv.org/abs/1612.08242v1	http://arxiv.org/pdf/1612.08242v1.pdf	https://github.com/trongnghia00/darknet	false	false	true	none
https://paperswithcode.com/paper/single-shot-refinement-neural-network-for	Single-Shot Refinement Neural Network for Object Detection	1711.06897	http://arxiv.org/abs/1711.06897v3	http://arxiv.org/pdf/1711.06897v3.pdf	https://github.com/laycoding/FaceDetection	false	false	true	none
https://paperswithcode.com/paper/pylearn2-a-machine-learning-research-library	Pylearn2: a machine learning research library	1308.4214	http://arxiv.org/abs/1308.4214v1	http://arxiv.org/pdf/1308.4214v1.pdf	https://github.com/jacobpeplinskiV2/pylearn2	false	false	true	none
https://paperswithcode.com/paper/which-encoding-is-the-best-for-text	Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?	1708.02657	http://arxiv.org/abs/1708.02657v2	http://arxiv.org/pdf/1708.02657v2.pdf	https://github.com/zhangxiangxiao/glyph	false	false	true	none
https://paperswithcode.com/paper/fooling-lidar-perception-via-adversarial	Fooling LiDAR Perception via Adversarial Trajectory Perturbation	2103.15326	https://arxiv.org/abs/2103.15326v2	https://arxiv.org/pdf/2103.15326v2.pdf	https://github.com/ai4ce/FLAT	false	false	true	pytorch
https://paperswithcode.com/paper/density-estimation-using-real-nvp	Density estimation using Real NVP	1605.08803	http://arxiv.org/abs/1605.08803v3	http://arxiv.org/pdf/1605.08803v3.pdf	https://github.com/ANLGBOY/RealNVP-with-PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/graph-to-sequence-learning-using-gated-graph	Graph-to-Sequence Learning using Gated Graph Neural Networks	1806.09835	http://arxiv.org/abs/1806.09835v1	http://arxiv.org/pdf/1806.09835v1.pdf	https://github.com/beckdaniel/acl2018_graph2seq	true	true	false	mxnet
https://paperswithcode.com/paper/robust-probabilistic-modeling-with-bayesian	Robust Probabilistic Modeling with Bayesian Data Reweighting	1606.03860	http://arxiv.org/abs/1606.03860v3	http://arxiv.org/pdf/1606.03860v3.pdf	https://github.com/yixinwang/robust-rpm-public	false	false	true	none
https://paperswithcode.com/paper/fine-grained-analysis-of-sentence-embeddings	Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks	1608.04207	http://arxiv.org/abs/1608.04207v3	http://arxiv.org/pdf/1608.04207v3.pdf	https://github.com/facebookresearch/InferSent	false	false	true	pytorch
https://paperswithcode.com/paper/a-human-computer-interface-design-for	A Human-Computer Interface Design for Quantitative Measure of Regret Theory	1810.00462	http://arxiv.org/abs/1810.00462v1	http://arxiv.org/pdf/1810.00462v1.pdf	https://github.com/I2RLab/RegretMeasurement-GUI	false	false	true	none
https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement	Asynchronous Methods for Deep Reinforcement Learning	1602.01783	http://arxiv.org/abs/1602.01783v2	http://arxiv.org/pdf/1602.01783v2.pdf	https://github.com/ShibiHe/Q-Optimality-Tightening	false	false	true	none
https://paperswithcode.com/paper/facenet-a-unified-embedding-for-face	FaceNet: A Unified Embedding for Face Recognition and Clustering	1503.03832	http://arxiv.org/abs/1503.03832v3	http://arxiv.org/pdf/1503.03832v3.pdf	https://github.com/CharlesPikachu/CharlesFace	false	false	true	pytorch
https://paperswithcode.com/paper/playgol-learning-programs-through-play	Playgol: learning programs through play	1904.08993	https://arxiv.org/abs/1904.08993v2	https://arxiv.org/pdf/1904.08993v2.pdf	https://github.com/metagol/metagol	true	true	false	none
https://paperswithcode.com/paper/richer-convolutional-features-for-edge	Richer Convolutional Features for Edge Detection	1612.02103	https://arxiv.org/abs/1612.02103v3	https://arxiv.org/pdf/1612.02103v3.pdf	https://github.com/meteorshowers/RCF-pytorch	false	false	false	pytorch
https://paperswithcode.com/paper/first-steps-toward-camera-model	First Steps Toward Camera Model Identification with Convolutional Neural Networks	1603.01068	http://arxiv.org/abs/1603.01068v2	http://arxiv.org/pdf/1603.01068v2.pdf	https://github.com/polimi-ispl/camera-model-identification-with-cnn	false	false	false	caffe2
https://paperswithcode.com/paper/deriving-machine-attention-from-human	Deriving Machine Attention from Human Rationales	1808.09367	http://arxiv.org/abs/1808.09367v1	http://arxiv.org/pdf/1808.09367v1.pdf	https://github.com/Sein-Jang/Deriving-Machine-Attention-from-Human-Rationales	false	false	true	pytorch
https://paperswithcode.com/paper/convex-pentagons-that-admit-i-block	Convex pentagons that admit $i$-block transitive tilings	1510.01186	http://arxiv.org/abs/1510.01186v1	http://arxiv.org/pdf/1510.01186v1.pdf	https://github.com/justinjk007/Pentagonal-tiling	false	false	true	none
https://paperswithcode.com/paper/learning-to-see-in-the-dark	Learning to See in the Dark	1805.01934	http://arxiv.org/abs/1805.01934v1	http://arxiv.org/pdf/1805.01934v1.pdf	https://github.com/cydonia999/Learning_to_See_in_the_Dark_PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/improved-adversarial-systems-for-3d-object	Improved Adversarial Systems for 3D Object Generation and Reconstruction	1707.09557	http://arxiv.org/abs/1707.09557v3	http://arxiv.org/pdf/1707.09557v3.pdf	https://github.com/kingcheng2000/3D-IWGAN	false	false	true	none
https://paperswithcode.com/paper/uncertainty-aware-joint-salient-object-and	Uncertainty-aware Joint Salient Object and Camouflaged Object Detection	2104.02628	https://arxiv.org/abs/2104.02628v1	https://arxiv.org/pdf/2104.02628v1.pdf	https://github.com/JingZhang617/Joint_COD_SOD	true	true	false	pytorch
https://paperswithcode.com/paper/brain-tumor-segmentation-with-deep-neural	Brain Tumor Segmentation with Deep Neural Networks	1505.03540	http://arxiv.org/abs/1505.03540v3	http://arxiv.org/pdf/1505.03540v3.pdf	https://github.com/IAmSuyogJadhav/Brainy	false	false	true	none
https://paperswithcode.com/paper/texygen-a-benchmarking-platform-for-text	Texygen: A Benchmarking Platform for Text Generation Models	1802.01886	http://arxiv.org/abs/1802.01886v1	http://arxiv.org/pdf/1802.01886v1.pdf	https://github.com/geek-ai/Texygen	true	true	true	tf
https://paperswithcode.com/paper/compar-optimized-multi-compiler-for-automatic	ComPar: Optimized Multi-Compiler for Automatic OpenMP S2S Parallelization	2005.13304	http://arxiv.org/abs/2005.13304v1	http://arxiv.org/pdf/2005.13304v1.pdf	https://github.com/Scientific-Computing-Lab-NRCN/compar	true	true	false	none
https://paperswithcode.com/paper/asynchronous-bidirectional-decoding-for	Asynchronous Bidirectional Decoding for Neural Machine Translation	1801.05122	http://arxiv.org/abs/1801.05122v2	http://arxiv.org/pdf/1801.05122v2.pdf	https://github.com/DeepLearnXMU/ABD-NMT	true	true	false	none
https://paperswithcode.com/paper/vr-sgd-a-simple-stochastic-variance-reduction	VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning	1802.09932	http://arxiv.org/abs/1802.09932v2	http://arxiv.org/pdf/1802.09932v2.pdf	https://github.com/jnhujnhu/VR-SGD	true	true	false	none
https://paperswithcode.com/paper/neural-machine-translation	Neural Machine Translation	1709.07809	http://arxiv.org/abs/1709.07809v1	http://arxiv.org/pdf/1709.07809v1.pdf	https://github.com/neulab/xnmt	true	true	false	none
https://paperswithcode.com/paper/stacked-attention-networks-for-image-question	Stacked Attention Networks for Image Question Answering	1511.02274	http://arxiv.org/abs/1511.02274v2	http://arxiv.org/pdf/1511.02274v2.pdf	https://github.com/zcyang/imageqa-san	false	false	true	none
https://paperswithcode.com/paper/playing-hard-exploration-games-by-watching	Playing hard exploration games by watching YouTube	1805.11592	http://arxiv.org/abs/1805.11592v2	http://arxiv.org/pdf/1805.11592v2.pdf	https://github.com/MaxSobolMark/HardRLWithYoutube	false	false	true	tf
https://paperswithcode.com/paper/task-agnostic-continual-learning-using-online	Task Agnostic Continual Learning Using Online Variational Bayes	1803.10123	http://arxiv.org/abs/1803.10123v3	http://arxiv.org/pdf/1803.10123v3.pdf	https://github.com/taldatech/tf-bgd	false	false	true	tf
https://paperswithcode.com/paper/on-the-design-of-deep-priors-for-unsupervised	On the Design of Deep Priors for Unsupervised Audio Restoration	2104.07161	https://arxiv.org/abs/2104.07161v1	https://arxiv.org/pdf/2104.07161v1.pdf	https://github.com/vivsivaraman/designaudiopriors	true	true	false	pytorch
https://paperswithcode.com/paper/decoding-the-style-and-bias-of-song-lyrics	Decoding the Style and Bias of Song Lyrics	1907.07818	https://arxiv.org/abs/1907.07818v1	https://arxiv.org/pdf/1907.07818v1.pdf	https://github.com/manashpratim/Decoding-the-Style-and-Bias-of-Song-Lyrics	true	true	false	none
https://paperswithcode.com/paper/improving-the-resolution-of-cnn-feature-maps	Improving the Resolution of CNN Feature Maps Efficiently with Multisampling	1805.10766	https://arxiv.org/abs/1805.10766v2	https://arxiv.org/pdf/1805.10766v2.pdf	https://github.com/ShayanPersonal/checkered-cnn	true	true	true	pytorch
https://paperswithcode.com/paper/dual-gaussian-based-variational-subspace	Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification	2008.02520	https://arxiv.org/abs/2008.02520v1	https://arxiv.org/pdf/2008.02520v1.pdf	https://github.com/TPCD/DG-VAE	true	false	true	pytorch
https://paperswithcode.com/paper/multi-level-visual-similarity-based	Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos	2109.08275	https://arxiv.org/abs/2109.08275v2	https://arxiv.org/pdf/2109.08275v2.pdf	https://github.com/revaludo/MEAL	true	true	false	tf
https://paperswithcode.com/paper/efficient-learning-for-deep-quantum-neural	Efficient Learning for Deep Quantum Neural Networks	1902.10445	http://arxiv.org/abs/1902.10445v1	http://arxiv.org/pdf/1902.10445v1.pdf	https://github.com/R8monaW/DeepQNN	true	true	false	none
https://paperswithcode.com/paper/point-classification-with-runge-kutta	Classification with Runge-Kutta networks and feature space augmentation	2104.02369	https://arxiv.org/abs/2104.02369v2	https://arxiv.org/pdf/2104.02369v2.pdf	https://github.com/ElisaGiesecke/augmented-RK-Nets	true	true	false	pytorch
https://paperswithcode.com/paper/coreference-resolution-with-entity	Coreference Resolution with Entity Equalization	null	https://aclanthology.org/P19-1066	https://aclanthology.org/P19-1066.pdf	https://github.com/kkjawz/coref-ee	false	true	false	tf
https://paperswithcode.com/paper/necola-towards-a-universal-field-level	NECOLA: Towards a Universal Field-level Cosmological Emulator	2111.02441	https://arxiv.org/abs/2111.02441v1	https://arxiv.org/pdf/2111.02441v1.pdf	https://github.com/HAWinther/MG-PICOLA-PUBLIC	true	true	false	none
https://paperswithcode.com/paper/perceptual-quality-assessment-of-smartphone	Perceptual Quality Assessment of Smartphone Photography	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.pdf	https://github.com/h4nwei/SPAQ	true	true	false	pytorch
https://paperswithcode.com/paper/llmjudge-llms-for-relevance-judgments	LLMJudge: LLMs for Relevance Judgments	2408.08896	https://arxiv.org/abs/2408.08896v1	https://arxiv.org/pdf/2408.08896v1.pdf	https://github.com/llm4eval/LLMJudge	true	false	false	none
https://paperswithcode.com/paper/mebow-monocular-estimation-of-body-1	MEBOW: Monocular Estimation of Body Orientation In the Wild	2011.13688	https://arxiv.org/abs/2011.13688v1	https://arxiv.org/pdf/2011.13688v1.pdf	https://github.com/ChenyanWu/MEBOW	false	false	false	pytorch
https://paperswithcode.com/paper/cr-gan-learning-complete-representations-for	CR-GAN: Learning Complete Representations for Multi-view Generation	1806.11191	http://arxiv.org/abs/1806.11191v1	http://arxiv.org/pdf/1806.11191v1.pdf	https://github.com/bluer555/CR-GAN	true	true	true	pytorch
https://paperswithcode.com/paper/sparse-classification-and-phase-transitions-a	Sparse Classification and Phase Transitions: A Discrete Optimization Perspective	1710.01352	http://arxiv.org/abs/1710.01352v1	http://arxiv.org/pdf/1710.01352v1.pdf	https://github.com/jeanpauphilet/SubsetSelectionCIO.jl	false	false	true	none
https://paperswithcode.com/paper/variational-dropout-sparsifies-deep-neural	Variational Dropout Sparsifies Deep Neural Networks	1701.05369	http://arxiv.org/abs/1701.05369v3	http://arxiv.org/pdf/1701.05369v3.pdf	https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn	false	false	true	tf
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style	A Neural Algorithm of Artistic Style	1508.06576	http://arxiv.org/abs/1508.06576v2	http://arxiv.org/pdf/1508.06576v2.pdf	https://github.com/ChetanTayal138/NeuralStyleTransfer	false	false	true	tf
https://paperswithcode.com/paper/deep-image-homography-estimation	Deep Image Homography Estimation	1606.03798	http://arxiv.org/abs/1606.03798v1	http://arxiv.org/pdf/1606.03798v1.pdf	https://github.com/mazenmel/Deep-homography-estimation-Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/generative-adversarial-networks	Generative Adversarial Networks	1406.2661	https://arxiv.org/abs/1406.2661v1	https://arxiv.org/pdf/1406.2661v1.pdf	https://github.com/vaisakh-shaj/DeepLearning	false	false	true	tf
https://paperswithcode.com/paper/keyframe-based-monocular-slam-design-survey	Keyframe-based monocular SLAM: design, survey, and future directions	1607.00470	http://arxiv.org/abs/1607.00470v2	http://arxiv.org/pdf/1607.00470v2.pdf	https://github.com/adioshun/gitBook_DeepSlam	false	false	true	none
https://paperswithcode.com/paper/fiesta-fast-incremental-euclidean-distance	FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots	1903.02144	http://arxiv.org/abs/1903.02144v1	http://arxiv.org/pdf/1903.02144v1.pdf	https://github.com/HKUST-Aerial-Robotics/FIESTA	false	false	true	none
https://paperswithcode.com/paper/variational-disentanglement-for-rare-event	Variational Disentanglement for Rare Event Modeling	2009.08541	https://arxiv.org/abs/2009.08541v5	https://arxiv.org/pdf/2009.08541v5.pdf	https://github.com/zidixiu/VIE	true	true	true	pytorch
https://paperswithcode.com/paper/lookahead-optimizer-k-steps-forward-1-step	Lookahead Optimizer: k steps forward, 1 step back	1907.08610	https://arxiv.org/abs/1907.08610v2	https://arxiv.org/pdf/1907.08610v2.pdf	https://github.com/alphadl/lookahead.pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/new-insights-into-black-bodies	New insights into black bodies	1201.1809	https://arxiv.org/abs/1201.1809v2	https://arxiv.org/pdf/1201.1809v2.pdf	https://github.com/mikecokina/elisa	false	false	true	none
https://paperswithcode.com/paper/geometry-based-data-generation	Geometry-Based Data Generation	1802.04927	http://arxiv.org/abs/1802.04927v4	http://arxiv.org/pdf/1802.04927v4.pdf	https://github.com/KrishnaswamyLab/SUGAR	true	false	true	none
https://paperswithcode.com/paper/reconstructing-functions-and-estimating	Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia	1910.03636	https://arxiv.org/abs/1910.03636v5	https://arxiv.org/pdf/1910.03636v5.pdf	https://github.com/Guo-Jian-Wang/refann	true	true	false	pytorch
https://paperswithcode.com/paper/object-detectors-emerge-in-deep-scene-cnns	Object Detectors Emerge in Deep Scene CNNs	1412.6856	http://arxiv.org/abs/1412.6856v2	http://arxiv.org/pdf/1412.6856v2.pdf	https://github.com/JepsonWong/CNN_Visualization	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/wtingda/DeepRLBreakout	false	false	true	tf
https://paperswithcode.com/paper/prediction-intervals-split-normal-mixture	Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles	2007.09670	https://arxiv.org/abs/2007.09670v1	https://arxiv.org/pdf/2007.09670v1.pdf	https://github.com/tarik/pi-snm-qde	true	true	true	pytorch
https://paperswithcode.com/paper/sequential-stratified-regeneration-mcmc-for	Sequential Stratified Regeneration: MCMC for Large State Spaces with an Application to Subgraph Count Estimation	2012.03879	https://arxiv.org/abs/2012.03879v3	https://arxiv.org/pdf/2012.03879v3.pdf	https://github.com/dccspeed/ripple	true	true	true	none
https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object	YOLOv4: Optimal Speed and Accuracy of Object Detection	2004.10934	https://arxiv.org/abs/2004.10934v1	https://arxiv.org/pdf/2004.10934v1.pdf	https://github.com/hhk7734/tensorflow-yolov4	false	false	false	tf
https://paperswithcode.com/paper/from-monte-carlo-to-las-vegas-improving	From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets	1711.08442	http://arxiv.org/abs/1711.08442v1	http://arxiv.org/pdf/1711.08442v1.pdf	https://github.com/PurdueMINDS/MCLV-RBM	true	true	false	pytorch
https://paperswithcode.com/paper/a-neural-conversational-model	A Neural Conversational Model	1506.05869	http://arxiv.org/abs/1506.05869v3	http://arxiv.org/pdf/1506.05869v3.pdf	https://github.com/hamil168/Chatbots	false	false	true	tf
https://paperswithcode.com/paper/quantile-propagation-for-wasserstein	Quantile Propagation for Wasserstein-Approximate Gaussian Processes	1912.10200	https://arxiv.org/abs/1912.10200v3	https://arxiv.org/pdf/1912.10200v3.pdf	https://github.com/RuiZhang2016/Quantile-Propagation-for-Wasserstein-Approximate-Gaussian-Processes	true	true	true	none
https://paperswithcode.com/paper/linguistic-features-for-readability	Linguistic Features for Readability Assessment	2006.00377	https://arxiv.org/abs/2006.00377v1	https://arxiv.org/pdf/2006.00377v1.pdf	https://github.com/TovlyDeutsch/Linguistic-Features-for-Readability	true	true	true	pytorch
https://paperswithcode.com/paper/gain-missing-data-imputation-using-generative	GAIN: Missing Data Imputation using Generative Adversarial Nets	1806.02920	http://arxiv.org/abs/1806.02920v1	http://arxiv.org/pdf/1806.02920v1.pdf	https://github.com/dhanajitb/GAIN-Pytorch	false	false	false	pytorch
https://paperswithcode.com/paper/generative-adversarial-networks	Generative Adversarial Networks	1406.2661	https://arxiv.org/abs/1406.2661v1	https://arxiv.org/pdf/1406.2661v1.pdf	https://github.com/pskrunner14/face-DCGAN	false	false	true	tf
https://paperswithcode.com/paper/seboost-boosting-stochastic-learning-using	SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques	1609.00629	http://arxiv.org/abs/1609.00629v1	http://arxiv.org/pdf/1609.00629v1.pdf	https://github.com/eladrich/seboost	true	true	false	torch
https://paperswithcode.com/paper/binarized-neural-networks-training-deep	Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1	1602.02830	http://arxiv.org/abs/1602.02830v3	http://arxiv.org/pdf/1602.02830v3.pdf	https://github.com/csyhhu/MetaQuant	false	false	true	pytorch
https://paperswithcode.com/paper/refer360-circ-a-referring-expression	Refer360$^\circ$: A Referring Expression Recognition Dataset in 360$^\circ$ Images	null	https://aclanthology.org/2020.acl-main.644	https://aclanthology.org/2020.acl-main.644.pdf	https://github.com/volkancirik/refer360	false	false	false	pytorch
https://paperswithcode.com/paper/improving-patch-based-scene-text-script	Improving patch-based scene text script identification with ensembles of conjoined networks	1602.07480	http://arxiv.org/abs/1602.07480v2	http://arxiv.org/pdf/1602.07480v2.pdf	https://github.com/lluisgomez/script_identification	true	true	false	none
https://paperswithcode.com/paper/ladn-local-adversarial-disentangling-network	LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup	1904.11272	https://arxiv.org/abs/1904.11272v2	https://arxiv.org/pdf/1904.11272v2.pdf	https://github.com/wangguanzhi/LADN	false	false	true	pytorch
https://paperswithcode.com/paper/stackgan-text-to-photo-realistic-image	StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks	1612.03242	http://arxiv.org/abs/1612.03242v2	http://arxiv.org/pdf/1612.03242v2.pdf	https://github.com/dhirajpatnaik16297/IMG-TXT-Generative-Adversarial-Network	false	false	true	tf
https://paperswithcode.com/paper/atomnas-fine-grained-end-to-end-neural-1	AtomNAS: Fine-Grained End-to-End Neural Architecture Search	1912.09640	https://arxiv.org/abs/1912.09640v2	https://arxiv.org/pdf/1912.09640v2.pdf	https://github.com/meijieru/AtomNAS	true	true	true	pytorch
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/orkqueen/depplabseongil	false	false	true	tf

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.