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/an-exploration-of-embodied-visual-exploration	An Exploration of Embodied Visual Exploration	2001.02192	https://arxiv.org/abs/2001.02192v2	https://arxiv.org/pdf/2001.02192v2.pdf	https://github.com/facebookresearch/exploring_exploration	true	true	false	pytorch
https://paperswithcode.com/paper/understanding-the-disharmony-between-weight	Understanding the Disharmony between Weight Normalization Family and Weight Decay: $ε-$shifted $L_2$ Regularizer	1911.05920	https://arxiv.org/abs/1911.05920v1	https://arxiv.org/pdf/1911.05920v1.pdf	https://github.com/implus/PytorchInsight	true	true	false	pytorch
https://paperswithcode.com/paper/learning-deconvolution-network-for-semantic	Learning Deconvolution Network for Semantic Segmentation	1505.04366	http://arxiv.org/abs/1505.04366v1	http://arxiv.org/pdf/1505.04366v1.pdf	https://github.com/GoNgXiAoPeNg1/caffeBVLCplus	false	false	true	none
https://paperswithcode.com/paper/parameter-constrained-transfer-learning-for	Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising	1910.06749	https://arxiv.org/abs/1910.06749v3	https://arxiv.org/pdf/1910.06749v3.pdf	https://github.com/90n9-yu/PT-WGAN	true	true	false	tf
https://paperswithcode.com/paper/deep-multi-view-learning-via-task-optimal-cca	Deep Multi-View Learning via Task-Optimal CCA	1907.07739	https://arxiv.org/abs/1907.07739v1	https://arxiv.org/pdf/1907.07739v1.pdf	https://github.com/hdcouture/TOCCA	false	false	true	tf
https://paperswithcode.com/paper/tar-generalized-forensic-framework-to-detect	TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning	2105.06117	https://arxiv.org/abs/2105.06117v1	https://arxiv.org/pdf/2105.06117v1.pdf	https://github.com/Clench/TAR_resAE	true	true	false	none
https://paperswithcode.com/paper/a-divide-and-conquer-algorithm-for-quantum	A divide-and-conquer algorithm for quantum state preparation	2008.01511	https://arxiv.org/abs/2008.01511v2	https://arxiv.org/pdf/2008.01511v2.pdf	https://github.com/adjs/dcsp	false	false	true	none
https://paperswithcode.com/paper/learning-to-navigate-image-manifolds-induced	Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation	1901.11384	http://arxiv.org/abs/1901.11384v1	http://arxiv.org/pdf/1901.11384v1.pdf	https://github.com/belaalb/frameGAN	true	true	true	pytorch
https://paperswithcode.com/paper/mine-mutual-information-neural-estimation	MINE: Mutual Information Neural Estimation	1801.04062	https://arxiv.org/abs/1801.04062v5	https://arxiv.org/pdf/1801.04062v5.pdf	https://github.com/mboudiaf/Mutual-Information-Variational-Bounds	false	false	true	tf
https://paperswithcode.com/paper/representation-learning-with-contrastive	Representation Learning with Contrastive Predictive Coding	1807.03748	http://arxiv.org/abs/1807.03748v2	http://arxiv.org/pdf/1807.03748v2.pdf	https://github.com/mboudiaf/Mutual-Information-Variational-Bounds	false	false	true	tf
https://paperswithcode.com/paper/a-convnet-for-the-2020s	A ConvNet for the 2020s	2201.03545	https://arxiv.org/abs/2201.03545v2	https://arxiv.org/pdf/2201.03545v2.pdf	https://github.com/keras-team/keras/blob/master/keras/applications/convnext.py	false	false	false	tf
https://paperswithcode.com/paper/vilbert-pretraining-task-agnostic	ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks	1908.02265	https://arxiv.org/abs/1908.02265v1	https://arxiv.org/pdf/1908.02265v1.pdf	https://github.com/jialinwu17/tmpimgs	false	false	true	pytorch
https://paperswithcode.com/paper/an-ensemble-based-approach-to-click-through	An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy	1711.01377	http://arxiv.org/abs/1711.01377v2	http://arxiv.org/pdf/1711.01377v2.pdf	https://github.com/cpapadimitriou/Click-Through-Rate-prediction	false	false	true	none
https://paperswithcode.com/paper/squeezedet-unified-small-low-power-fully	SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving	1612.01051	https://arxiv.org/abs/1612.01051v4	https://arxiv.org/pdf/1612.01051v4.pdf	https://github.com/vahidkhosh/squeezedet-keras	false	false	true	none
https://paperswithcode.com/paper/deep-generative-networks-for-sequence	Deep Generative Networks For Sequence Prediction	1804.06546	http://arxiv.org/abs/1804.06546v1	http://arxiv.org/pdf/1804.06546v1.pdf	https://github.com/mbeissinger/recurrent_gsn	true	true	false	none
https://paperswithcode.com/paper/android-malware-family-classification-based	Android Malware Family Classification Based on Resource Consumption over Time	1709.00875	https://arxiv.org/abs/1709.00875v1	https://arxiv.org/pdf/1709.00875v1.pdf	https://github.com/lucamassarelli/AMFC-BRCT	true	true	false	none
https://paperswithcode.com/paper/grid-tagging-scheme-for-aspect-oriented-fine	Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction	2010.04640	https://arxiv.org/abs/2010.04640v2	https://arxiv.org/pdf/2010.04640v2.pdf	https://github.com/l294265421/GTS-ASOTE	false	false	true	pytorch
https://paperswithcode.com/paper/fcos-fully-convolutional-one-stage-object	FCOS: Fully Convolutional One-Stage Object Detection	1904.01355	https://arxiv.org/abs/1904.01355v5	https://arxiv.org/pdf/1904.01355v5.pdf	https://github.com/vov-net/VoVNet-FCOS	false	false	true	pytorch
https://paperswithcode.com/paper/retinal-vessel-segmentation-based-on-fully-1	Retinal Vessel Segmentation based on Fully Convolutional Networks	1911.09915	https://arxiv.org/abs/1911.09915v1	https://arxiv.org/pdf/1911.09915v1.pdf	https://github.com/americofmoliveira/VesselSegmentation_ESWA	true	true	false	none
https://paperswithcode.com/paper/sentences-with-gapping-parsing-and	Sentences with Gapping: Parsing and Reconstructing Elided Predicates	1804.06922	http://arxiv.org/abs/1804.06922v1	http://arxiv.org/pdf/1804.06922v1.pdf	https://github.com/dialogue-evaluation/AGRR-2019	false	false	true	none
https://paperswithcode.com/paper/an-empirical-comparison-between-stochastic	An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations	1908.09946	https://arxiv.org/abs/1908.09946v6	https://arxiv.org/pdf/1908.09946v6.pdf	https://github.com/avouros/clustering-workplace	false	false	true	none
https://paperswithcode.com/paper/joint-semantic-mining-for-weakly-supervised	Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection	null	http://proceedings.neurips.cc/paper/2021/hash/642e92efb79421734881b53e1e1b18b6-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/642e92efb79421734881b53e1e1b18b6-Paper.pdf	https://github.com/jiwei0921/jsm	true	true	false	pytorch
https://paperswithcode.com/paper/fair-dimensionality-reduction-and-iterative	Multi-Criteria Dimensionality Reduction with Applications to Fairness	1902.11281	https://arxiv.org/abs/1902.11281v3	https://arxiv.org/pdf/1902.11281v3.pdf	https://github.com/SDPforAll/multiCriteriaDimReduction	true	true	true	none
https://paperswithcode.com/paper/190109195	Variational approach to rare event simulation using least-squares regression	1901.09195	http://arxiv.org/abs/1901.09195v2	http://arxiv.org/pdf/1901.09195v2.pdf	https://github.com/lorenzrichter/BSDE	true	true	true	none
https://paperswithcode.com/paper/smart-contract-vulnerabilities-does-anyone	Smart Contract Vulnerabilities: Does Anyone Care?	1902.06710	http://arxiv.org/abs/1902.06710v2	http://arxiv.org/pdf/1902.06710v2.pdf	https://github.com/danhper/evm-analyzer	true	false	true	none
https://paperswithcode.com/paper/proximal-mean-field-for-neural-network	Proximal Mean-field for Neural Network Quantization	1812.04353	https://arxiv.org/abs/1812.04353v3	https://arxiv.org/pdf/1812.04353v3.pdf	https://github.com/tajanthan/pmf	true	true	true	pytorch
https://paperswithcode.com/paper/development-and-application-of-a	Development and Application of a Decentralized Domain Name Service	2412.01959	https://arxiv.org/abs/2412.01959v2	https://arxiv.org/pdf/2412.01959v2.pdf	https://github.com/GY19A/ddns	true	false	false	none
https://paperswithcode.com/paper/joint-ranking-svm-and-binary-relevance-with	Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification	1911.01658	https://arxiv.org/abs/1911.01658v1	https://arxiv.org/pdf/1911.01658v1.pdf	https://github.com/GuoqiangWoodrowWu/RBRL	true	true	false	none
https://paperswithcode.com/paper/scale-wise-convolution-for-image-restoration	Scale-wise Convolution for Image Restoration	1912.09028	https://arxiv.org/abs/1912.09028v1	https://arxiv.org/pdf/1912.09028v1.pdf	https://github.com/ychfan/scn	true	true	true	pytorch
https://paperswithcode.com/paper/deep-convolutional-neural-networks-for-12	Deep Convolutional Neural Networks for Thermal Infrared Object Tracking	null	https://www.sciencedirect.com/science/article/abs/pii/S0950705117303544	https://www.researchgate.net/publication/318714772_Deep_Convolutional_Neural_Networks_for_Thermal_Infrared_Object_Tracking	https://github.com/QiaoLiuHit/MCFTS	false	false	false	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/flora-zyx/SJNet	false	false	true	pytorch
https://paperswithcode.com/paper/measuring-spatial-allocative-efficiency-in	Measuring Spatial Allocative Efficiency in Basketball	1912.05129	https://arxiv.org/abs/1912.05129v2	https://arxiv.org/pdf/1912.05129v2.pdf	https://github.com/nsandholtz/lpl	true	true	true	none
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/karuj/StyleTransfer	false	false	true	pytorch
https://paperswithcode.com/paper/generalized-latency-performance-estimation	Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search	2101.00732	https://arxiv.org/abs/2101.00732v1	https://arxiv.org/pdf/2101.00732v1.pdf	https://github.com/RhythmSyed/NAS_PerformanceEstimation	false	false	false	pytorch
https://paperswithcode.com/paper/physically-disentangled-intra-and-inter	Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal	null	http://openaccess.thecvf.com//content/CVPR2022/html/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.pdf	https://github.com/huayuuu/pdi2a-cvpr2022	true	true	false	none
https://paperswithcode.com/paper/boosted-cascaded-convnets-for-multilabel	Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs	1711.08760	http://arxiv.org/abs/1711.08760v1	http://arxiv.org/pdf/1711.08760v1.pdf	https://github.com/Azure/AzureChestXRay	false	false	true	pytorch
https://paperswithcode.com/paper/bayesian-segnet-model-uncertainty-in-deep	Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding	1511.02680	http://arxiv.org/abs/1511.02680v2	http://arxiv.org/pdf/1511.02680v2.pdf	https://github.com/SkyWa7ch3r/ImageSegmentation	false	false	true	tf
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/SkyWa7ch3r/ImageSegmentation	false	false	true	tf
https://paperswithcode.com/paper/fast-scnn-fast-semantic-segmentation-network	Fast-SCNN: Fast Semantic Segmentation Network	1902.04502	http://arxiv.org/abs/1902.04502v1	http://arxiv.org/pdf/1902.04502v1.pdf	https://github.com/SkyWa7ch3r/ImageSegmentation	false	false	true	tf
https://paperswithcode.com/paper/drop-an-octave-reducing-spatial-redundancy-in	Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution	1904.05049	https://arxiv.org/abs/1904.05049v3	https://arxiv.org/pdf/1904.05049v3.pdf	https://github.com/tuanzhangCS/octconv_resnet	false	false	true	tf
https://paperswithcode.com/paper/semi-supervised-sequence-learning	Semi-supervised Sequence Learning	1511.01432	http://arxiv.org/abs/1511.01432v1	http://arxiv.org/pdf/1511.01432v1.pdf	https://github.com/Zehui127/SQUAD_BERT	false	false	true	tf
https://paperswithcode.com/paper/weird-faccts-how-western-educated	WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?	2305.06415	https://arxiv.org/abs/2305.06415v1	https://arxiv.org/pdf/2305.06415v1.pdf	https://github.com/aliakbars/weird-facct	true	true	false	none
https://paperswithcode.com/paper/hypernetworks	HyperNetworks	1609.09106	http://arxiv.org/abs/1609.09106v4	http://arxiv.org/pdf/1609.09106v4.pdf	https://github.com/gahaalt/continual-learning-overview	false	false	true	tf
https://paperswithcode.com/paper/three-scenarios-for-continual-learning	Three scenarios for continual learning	1904.07734	http://arxiv.org/abs/1904.07734v1	http://arxiv.org/pdf/1904.07734v1.pdf	https://github.com/gahaalt/continual-learning-overview	false	false	true	tf
https://paperswithcode.com/paper/detecting-dga-domains-with-recurrent-neural	Detecting DGA domains with recurrent neural networks and side information	1810.02023	https://arxiv.org/abs/1810.02023v2	https://arxiv.org/pdf/1810.02023v2.pdf	https://github.com/alistairwgillespie/deep_dga_detection	false	false	true	pytorch
https://paperswithcode.com/paper/accelerating-the-super-resolution	Accelerating the Super-Resolution Convolutional Neural Network	1608.00367	http://arxiv.org/abs/1608.00367v1	http://arxiv.org/pdf/1608.00367v1.pdf	https://github.com/yjn870/FSRCNN-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/deep-learning-with-topological-signatures	Deep Learning with Topological Signatures	1707.04041	http://arxiv.org/abs/1707.04041v3	http://arxiv.org/pdf/1707.04041v3.pdf	https://github.com/billy-mosse/spiderman	false	false	true	none
https://paperswithcode.com/paper/sql-rank-a-listwise-approach-to-collaborative	SQL-Rank: A Listwise Approach to Collaborative Ranking	1803.00114	http://arxiv.org/abs/1803.00114v3	http://arxiv.org/pdf/1803.00114v3.pdf	https://github.com/wuliwei9278/SQL-Rank	true	true	false	none
https://paperswithcode.com/paper/drop-a-reading-comprehension-benchmark	DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs	1903.00161	http://arxiv.org/abs/1903.00161v2	http://arxiv.org/pdf/1903.00161v2.pdf	https://github.com/m3yrin/naqanet_notebook	false	false	true	none
https://paperswithcode.com/paper/on-finding-gray-pixels	On Finding Gray Pixels	1901.03198	https://arxiv.org/abs/1901.03198v3	https://arxiv.org/pdf/1901.03198v3.pdf	https://github.com/mahmoudnafifi/SIIE	false	false	true	tf
https://paperswithcode.com/paper/efficient-selection-of-predictive-biomarkers	Efficient selection of predictive biomarkers for individual treatment selection	1905.01582	https://arxiv.org/abs/1905.01582v1	https://arxiv.org/pdf/1905.01582v1.pdf	https://github.com/sshonosuke/SB-ITS	false	false	true	none
https://paperswithcode.com/paper/a-hierarchical-model-of-non-homogeneous	A hierarchical model of non-homogeneous Poisson processes for Twitter retweets	1802.01987	http://arxiv.org/abs/1802.01987v2	http://arxiv.org/pdf/1802.01987v2.pdf	https://github.com/clement-lee/hybridProcess	false	false	true	none
https://paperswithcode.com/paper/discontinuous-constituent-parsing-with	Discontinuous Constituent Parsing with Pointer Networks	2002.01824	https://arxiv.org/abs/2002.01824v1	https://arxiv.org/pdf/2002.01824v1.pdf	https://github.com/danifg/DiscoPointer	true	true	false	pytorch
https://paperswithcode.com/paper/wikihow-a-large-scale-text-summarization	WikiHow: A Large Scale Text Summarization Dataset	1810.09305	http://arxiv.org/abs/1810.09305v1	http://arxiv.org/pdf/1810.09305v1.pdf	https://github.com/LubdaMax/Data-Science-1	false	false	true	tf
https://paperswithcode.com/paper/computing-stable-models-of-normal-logic	Computing Stable Models of Normal Logic Programs Without Grounding	1709.00501	https://arxiv.org/abs/1709.00501v1	https://arxiv.org/pdf/1709.00501v1.pdf	https://github.com/sarat-chandra-varanasi/pysasp	false	false	true	none
https://paperswithcode.com/paper/unsupervised-domain-adaptation-through-self-1	Unsupervised Domain Adaptation through Self-Supervision	1909.11825	https://arxiv.org/abs/1909.11825v2	https://arxiv.org/pdf/1909.11825v2.pdf	https://github.com/Jinsung-Jeon/DomainAdaptation	false	false	true	pytorch
https://paperswithcode.com/paper/joint-learning-of-the-embedding-of-words-and	Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation	1601.01343	http://arxiv.org/abs/1601.01343v4	http://arxiv.org/pdf/1601.01343v4.pdf	https://github.com/wikipedia2vec/wikipedia2vec	false	false	true	none
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/KarthikGangadhar/depth-estimation	false	false	true	tf
https://paperswithcode.com/paper/towards-a-general-purpose-cnn-for-long-range	Towards a General Purpose CNN for Long Range Dependencies in $N$D	2206.03398	https://arxiv.org/abs/2206.03398v2	https://arxiv.org/pdf/2206.03398v2.pdf	https://github.com/david-knigge/ccnn	true	true	true	pytorch
https://paperswithcode.com/paper/axiomatic-ranking-of-network-role-similarity	Axiomatic Ranking of Network Role Similarity	1102.3937	https://arxiv.org/abs/1102.3937v2	https://arxiv.org/pdf/1102.3937v2.pdf	https://github.com/abhishekmaha23/RoleSim_python	false	false	true	none
https://paperswithcode.com/paper/neural-machine-translation-by-jointly	Neural Machine Translation by Jointly Learning to Align and Translate	1409.0473	http://arxiv.org/abs/1409.0473v7	http://arxiv.org/pdf/1409.0473v7.pdf	https://github.com/mike-a-yen/date-translation	false	false	true	pytorch
https://paperswithcode.com/paper/explainable-neural-computation-via-stack	Explainable Neural Computation via Stack Neural Module Networks	1807.08556	http://arxiv.org/abs/1807.08556v3	http://arxiv.org/pdf/1807.08556v3.pdf	https://github.com/ronghanghu/snmn	false	false	true	tf
https://paperswithcode.com/paper/dftatom-a-robust-and-general-schrodinger-and	dftatom: A robust and general Schrödinger and Dirac solver for atomic structure calculations	1209.1752	http://arxiv.org/abs/1209.1752v2	http://arxiv.org/pdf/1209.1752v2.pdf	https://github.com/certik/dftatom	false	false	true	tf
https://paperswithcode.com/paper/friendship-paradox-biases-perceptions-in	Friendship Paradox Biases Perceptions in Directed Networks	1905.05286	https://arxiv.org/abs/1905.05286v1	https://arxiv.org/pdf/1905.05286v1.pdf	https://github.com/ninoch/perception_bias	true	false	true	none
https://paperswithcode.com/paper/local-global-fusion-network-for-video-super	Local-Global Fusion Network for Video Super-Resolution	null	https://ieeexplore.ieee.org/document/9203860/authors#authors	https://ieeexplore.ieee.org/document/9203860/authors#authors	https://github.com/BIOINSu/LGFN	false	true	false	pytorch
https://paperswithcode.com/paper/learning-new-physics-from-a-machine	Learning New Physics from a Machine	1806.02350	https://arxiv.org/abs/1806.02350v1	https://arxiv.org/pdf/1806.02350v1.pdf	https://github.com/gvlos/LNPFM	false	false	true	none
https://paperswithcode.com/paper/on-network-design-spaces-for-visual	On Network Design Spaces for Visual Recognition	1905.13214	https://arxiv.org/abs/1905.13214v1	https://arxiv.org/pdf/1905.13214v1.pdf	https://github.com/tuggeluk/pycls	false	false	true	pytorch
https://paperswithcode.com/paper/unsupervised-feature-learning-via-non	Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination	1805.01978	http://arxiv.org/abs/1805.01978v1	http://arxiv.org/pdf/1805.01978v1.pdf	https://github.com/zhirongw/lemniscate.pytorch	true	true	true	pytorch
https://paperswithcode.com/paper/towards-k-means-friendly-spaces-simultaneous	Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering	1610.04794	http://arxiv.org/abs/1610.04794v2	http://arxiv.org/pdf/1610.04794v2.pdf	https://github.com/XiaoxiangLin/DCN-keras	false	false	true	none
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/catalyst-team/gan	false	false	true	pytorch
https://paperswithcode.com/paper/modeling-and-control-of-a-reconfigurable	Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning	1907.08023	https://arxiv.org/abs/1907.08023v1	https://arxiv.org/pdf/1907.08023v1.pdf	https://github.com/akramyoussry/GRUBI	true	true	true	tf
https://paperswithcode.com/paper/a-very-low-resource-language-speech-corpus	A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments	1710.03501	http://arxiv.org/abs/1710.03501v3	http://arxiv.org/pdf/1710.03501v3.pdf	https://github.com/mzboito/mmboshi	false	false	true	none
https://paperswithcode.com/paper/empirical-evaluation-of-scoring-functions-for	Empirical evaluation of scoring functions for Bayesian network model selection	null	https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-S15-S14#Abs1	https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-13-S15-S14	https://github.com/miladce/Bayesian-network-learning	false	false	false	none
https://paperswithcode.com/paper/hierarchical-human-parsing-with-typed-part	Hierarchical Human Parsing with Typed Part-Relation Reasoning	2003.04845	https://arxiv.org/abs/2003.04845v2	https://arxiv.org/pdf/2003.04845v2.pdf	https://github.com/hlzhu09/Hierarchical-Human-Parsing	true	true	false	pytorch
https://paperswithcode.com/paper/continuous-control-with-deep-reinforcement	Continuous control with deep reinforcement learning	1509.02971	https://arxiv.org/abs/1509.02971v6	https://arxiv.org/pdf/1509.02971v6.pdf	https://github.com/prajwalgatti/DRL-Continuous-Control	false	false	true	none
https://paperswithcode.com/paper/fine-grained-visual-classification-via	Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches	2003.03836	https://arxiv.org/abs/2003.03836v3	https://arxiv.org/pdf/2003.03836v3.pdf	https://github.com/RuoyiDu/PMG-Progressive-Multi-Granularity-Training	true	true	false	pytorch
https://paperswithcode.com/paper/mfes-hb-efficient-hyperband-with-multi	MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements	2012.03011	https://arxiv.org/abs/2012.03011v2	https://arxiv.org/pdf/2012.03011v2.pdf	https://github.com/thomas-young-2013/open-box	false	false	true	none
https://paperswithcode.com/paper/openbox-a-generalized-black-box-optimization	OpenBox: A Generalized Black-box Optimization Service	2106.00421	https://arxiv.org/abs/2106.00421v3	https://arxiv.org/pdf/2106.00421v3.pdf	https://github.com/thomas-young-2013/open-box	false	false	true	none
https://paperswithcode.com/paper/spotting-macro-and-micro-expression-intervals	Spotting Macro- and Micro-expression Intervals in Long Video Sequences	1912.11985	https://arxiv.org/abs/1912.11985v3	https://arxiv.org/pdf/1912.11985v3.pdf	https://github.com/HeyingGithub/Baseline-project-for-MEGC2020_spotting	true	true	true	none
https://paperswithcode.com/paper/keyfilter-aware-real-time-uav-object-tracking	Keyfilter-Aware Real-Time UAV Object Tracking	2003.05218	https://arxiv.org/abs/2003.05218v1	https://arxiv.org/pdf/2003.05218v1.pdf	https://github.com/vision4robotics/KAOT-tracker	true	true	false	none
https://paperswithcode.com/paper/energy-aware-coverage-planning-for	Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System	2411.02230	https://arxiv.org/abs/2411.02230v1	https://arxiv.org/pdf/2411.02230v1.pdf	https://github.com/herolab-uga/energy-aware-coverage	true	false	true	none
https://paperswithcode.com/paper/advances-in-collaborative-filtering-and	Advances in Collaborative Filtering and Ranking	2002.12312	https://arxiv.org/abs/2002.12312v1	https://arxiv.org/pdf/2002.12312v1.pdf	https://github.com/wuliwei9278/SQL-Rank	true	true	false	none
https://paperswithcode.com/paper/a-clockwork-rnn	A Clockwork RNN	1402.3511	http://arxiv.org/abs/1402.3511v1	http://arxiv.org/pdf/1402.3511v1.pdf	https://github.com/html1101/Science-Fair-2019-2020	false	false	true	tf
https://paperswithcode.com/paper/approximating-network-centrality-measures	Approximating Network Centrality Measures Using Node Embedding and Machine Learning	2006.16392	https://arxiv.org/abs/2006.16392v4	https://arxiv.org/pdf/2006.16392v4.pdf	https://github.com/MatheusMRFM/NCA-GE	true	true	false	tf
https://paperswithcode.com/paper/power-law-distributions-in-empirical-data	Power-law distributions in empirical data	0706.1062	http://arxiv.org/abs/0706.1062v2	http://arxiv.org/pdf/0706.1062v2.pdf	https://github.com/jlapeyre/MaximumLikelihoodPower.jl	false	false	true	none
https://paperswithcode.com/paper/class-balanced-loss-based-on-effective-number	Class-Balanced Loss Based on Effective Number of Samples	1901.05555	http://arxiv.org/abs/1901.05555v1	http://arxiv.org/pdf/1901.05555v1.pdf	https://github.com/feidfoe/AdjustBnd4Imbalance	false	false	true	pytorch
https://paperswithcode.com/paper/fast-context-adaptation-via-meta-learning	Fast Context Adaptation via Meta-Learning	1810.03642	https://arxiv.org/abs/1810.03642v4	https://arxiv.org/pdf/1810.03642v4.pdf	https://github.com/lmzintgraf/cavia	true	true	true	pytorch
https://paperswithcode.com/paper/dual-attention-guided-dropblock-module-for	Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization	2003.04719	https://arxiv.org/abs/2003.04719v3	https://arxiv.org/pdf/2003.04719v3.pdf	https://github.com/cpuimage/DualAttentionGuidedDropout	false	false	true	tf
https://paperswithcode.com/paper/can-connected-autonomous-vehicles-really	Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?	2107.03078	https://arxiv.org/abs/2107.03078v2	https://arxiv.org/pdf/2107.03078v2.pdf	https://github.com/gargmohit24/ITSC_2021	true	true	false	none
https://paperswithcode.com/paper/contrastive-multiview-coding	Contrastive Multiview Coding	1906.05849	https://arxiv.org/abs/1906.05849v5	https://arxiv.org/pdf/1906.05849v5.pdf	https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning	false	false	true	pytorch
https://paperswithcode.com/paper/structural-regularities-in-text-based-entity	Structural Regularities in Text-based Entity Vector Spaces	1707.07930	http://arxiv.org/abs/1707.07930v1	http://arxiv.org/pdf/1707.07930v1.pdf	https://github.com/cvangysel/SERT	true	true	true	none
https://paperswithcode.com/paper/network-modelling-of-topological-domains	Network modelling of topological domains using Hi-C data	1707.09587	http://arxiv.org/abs/1707.09587v2	http://arxiv.org/pdf/1707.09587v2.pdf	https://github.com/zhongmicai/Hic_tools_collection	false	false	true	tf
https://paperswithcode.com/paper/pt2pc-learning-to-generate-3d-point-cloud	PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions	2003.08624	https://arxiv.org/abs/2003.08624v2	https://arxiv.org/pdf/2003.08624v2.pdf	https://github.com/daerduoCarey/pt2pc	false	false	true	pytorch
https://paperswithcode.com/paper/zero-shot-learning-a-comprehensive-evaluation	Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly	1707.00600	https://arxiv.org/abs/1707.00600v4	https://arxiv.org/pdf/1707.00600v4.pdf	https://github.com/vkverma01/Zero-Shot	false	false	true	none
https://paperswithcode.com/paper/robrose-a-robust-approach-for-dealing-with	robROSE: A robust approach for dealing with imbalanced data in fraud detection	2003.11915	https://arxiv.org/abs/2003.11915v1	https://arxiv.org/pdf/2003.11915v1.pdf	https://github.com/SebastiaanHoppner/robROSE	true	true	true	none
https://paperswithcode.com/paper/testing-of-deep-reinforcement-learning-agents	Testing of Deep Reinforcement Learning Agents with Surrogate Models	2305.12751	https://arxiv.org/abs/2305.12751v2	https://arxiv.org/pdf/2305.12751v2.pdf	https://github.com/matteobiagiola/drl-testing-experiments	true	true	false	none
https://paperswithcode.com/paper/faster-fast-tensor-completion-with-nonconvex	FasTer: Fast Tensor Completion with Nonconvex Regularization	1807.08725	http://arxiv.org/abs/1807.08725v3	http://arxiv.org/pdf/1807.08725v3.pdf	https://github.com/quanmingyao/FasTer	false	false	true	none
https://paperswithcode.com/paper/chatgpt-in-the-context-of-precision	ChatGPT in the context of precision agriculture data analytics	2311.06390	https://arxiv.org/abs/2311.06390v1	https://arxiv.org/pdf/2311.06390v1.pdf	https://github.com/potamitis123/chatgpt-in-the-context-of-precision-agriculture-data-analytics	true	true	false	none
https://paperswithcode.com/paper/fd-gan-pose-guided-feature-distilling-gan-for	FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification	1810.02936	http://arxiv.org/abs/1810.02936v2	http://arxiv.org/pdf/1810.02936v2.pdf	https://github.com/NVlabs/DG-Net	false	false	false	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/kenziyuliu/ms-g3d	false	false	true	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.