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/compositional-coding-capsule-network-with-k	Compositional Coding Capsule Network with K-Means Routing for Text Classification	1810.09177	https://arxiv.org/abs/1810.09177v5	https://arxiv.org/pdf/1810.09177v5.pdf	https://github.com/leftthomas/CCCapsNet	true	true	true	pytorch
https://paperswithcode.com/paper/bisenet-bilateral-segmentation-network-for	BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation	1808.00897	http://arxiv.org/abs/1808.00897v1	http://arxiv.org/pdf/1808.00897v1.pdf	https://github.com/kirilcvetkov92/Semantic-Segmentation	false	false	true	none
https://paperswithcode.com/paper/chinese-opinion-role-labeling-with-corpus	Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study	null	https://aclanthology.org/2021.emnlp-main.796	https://aclanthology.org/2021.emnlp-main.796.pdf	https://github.com/zenrran/chineseorl-with-corpus-translation	true	true	false	pytorch
https://paperswithcode.com/paper/dank-or-not-analyzing-and-predicting-the	Dank or Not? -- Analyzing and Predicting the Popularity of Memes on Reddit	2011.14326	https://arxiv.org/abs/2011.14326v2	https://arxiv.org/pdf/2011.14326v2.pdf	https://github.com/dimaTrinh/dank_data	true	true	true	none
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/jimheaton/Ultra96_ML_Embedded_Workshop	false	false	true	none
https://paperswithcode.com/paper/a-structured-self-attentive-sentence	A Structured Self-attentive Sentence Embedding	1703.03130	http://arxiv.org/abs/1703.03130v1	http://arxiv.org/pdf/1703.03130v1.pdf	https://github.com/natel9178/transformer-refork	false	false	true	pytorch
https://paperswithcode.com/paper/graph-degree-linkage-agglomerative-clustering	Graph Degree Linkage: Agglomerative Clustering on a Directed Graph	1208.5092	http://arxiv.org/abs/1208.5092v1	http://arxiv.org/pdf/1208.5092v1.pdf	https://github.com/waynezhanghk/gactoolbox	false	false	false	none
https://paperswithcode.com/paper/deeperase-weakly-supervised-ink-artifact	DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images	1910.07070	https://arxiv.org/abs/1910.07070v3	https://arxiv.org/pdf/1910.07070v3.pdf	https://github.com/Guillem96/deep-erase	false	false	true	tf
https://paperswithcode.com/paper/sequence-modeling-via-segmentations	Sequence Modeling via Segmentations	1702.07463	http://arxiv.org/abs/1702.07463v7	http://arxiv.org/pdf/1702.07463v7.pdf	https://github.com/posenhuang/NPMT	true	true	true	torch
https://paperswithcode.com/paper/pointrend-image-segmentation-as-rendering	PointRend: Image Segmentation as Rendering	1912.08193	https://arxiv.org/abs/1912.08193v2	https://arxiv.org/pdf/1912.08193v2.pdf	https://github.com/JamesQFreeman/PointRend	false	false	true	none
https://paperswithcode.com/paper/holophrasm-a-neural-automated-theorem-prover	Holophrasm: a neural Automated Theorem Prover for higher-order logic	1608.02644	http://arxiv.org/abs/1608.02644v2	http://arxiv.org/pdf/1608.02644v2.pdf	https://github.com/giomasce/mmpp	false	false	true	none
https://paperswithcode.com/paper/cloud-based-quadratic-optimization-with	Cloud-based Quadratic Optimization with Partially Homomorphic Encryption	1809.02267	http://arxiv.org/abs/1809.02267v1	http://arxiv.org/pdf/1809.02267v1.pdf	https://github.com/andreea-alexandru/QPHE	false	false	true	none
https://paperswithcode.com/paper/conditional-random-fields-as-recurrent-neural-1	Conditional Random Fields as Recurrent Neural Networks	1502.03240	http://arxiv.org/abs/1502.03240v3	http://arxiv.org/pdf/1502.03240v3.pdf	https://github.com/liyin2015/superpixel_crfasrnn	false	false	true	tf
https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	1602.07261	http://arxiv.org/abs/1602.07261v2	http://arxiv.org/pdf/1602.07261v2.pdf	https://github.com/92coorob/facerec2	false	false	true	tf
https://paperswithcode.com/paper/predicting-and-explaining-human-semantic	Predicting and Explaining Human Semantic Search in a Cognitive Model	1711.11125	http://arxiv.org/abs/1711.11125v1	http://arxiv.org/pdf/1711.11125v1.pdf	https://github.com/mkduer/semantic-fluency-nn	false	false	true	none
https://paperswithcode.com/paper/maximum-likelihood-estimation-of-quantum	Maximum-likelihood estimation of quantum measurement	quant-ph/0101027	https://arxiv.org/abs/quant-ph/0101027v2	https://arxiv.org/pdf/quant-ph/0101027v2.pdf	https://github.com/fbm2718/QREM	false	false	true	none
https://paperswithcode.com/paper/variational-continual-learning	Variational Continual Learning	1710.10628	http://arxiv.org/abs/1710.10628v3	http://arxiv.org/pdf/1710.10628v3.pdf	https://github.com/pihey1995/VariationalContinualLearning	false	false	true	torch
https://paperswithcode.com/paper/190205027	Proximity Queries for Absolutely Continuous Parametric Curves	1902.05027	http://arxiv.org/abs/1902.05027v2	http://arxiv.org/pdf/1902.05027v2.pdf	https://github.com/arlk/CurveProximityQueries.jl	true	true	true	none
https://paperswithcode.com/paper/discovering-human-interactions-with-novel	Discovering Human Interactions With Novel Objects via Zero-Shot Learning	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Discovering_Human_Interactions_With_Novel_Objects_via_Zero-Shot_Learning_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Discovering_Human_Interactions_With_Novel_Objects_via_Zero-Shot_Learning_CVPR_2020_paper.pdf	https://github.com/scwangdyd/zero_shot_hoi	true	true	false	pytorch
https://paperswithcode.com/paper/adversarial-transformation-networks-learning	Adversarial Transformation Networks: Learning to Generate Adversarial Examples	1703.09387	http://arxiv.org/abs/1703.09387v1	http://arxiv.org/pdf/1703.09387v1.pdf	https://github.com/pfnet-research/nips17-adversarial-attack	false	false	true	tf
https://paperswithcode.com/paper/stroke-based-artistic-rendering-agent-with	Learning to Paint With Model-based Deep Reinforcement Learning	1903.04411	https://arxiv.org/abs/1903.04411v3	https://arxiv.org/pdf/1903.04411v3.pdf	https://github.com/hzwer/LearningToPaint	false	false	true	pytorch
https://paperswithcode.com/paper/convmlp-hierarchical-convolutional-mlps-for	ConvMLP: Hierarchical Convolutional MLPs for Vision	2109.04454	https://arxiv.org/abs/2109.04454v2	https://arxiv.org/pdf/2109.04454v2.pdf	https://github.com/shinya7y/UniverseNet	false	false	false	pytorch
https://paperswithcode.com/paper/medoids-in-almost-linear-time-via-multi-armed	Medoids in almost linear time via multi-armed bandits	1711.00817	http://arxiv.org/abs/1711.00817v3	http://arxiv.org/pdf/1711.00817v3.pdf	https://github.com/bagavi/Meddit	true	true	false	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/allenai/allennlp-models	false	false	false	pytorch
https://paperswithcode.com/paper/what-uncertainties-do-we-need-in-bayesian	What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?	1703.04977	http://arxiv.org/abs/1703.04977v2	http://arxiv.org/pdf/1703.04977v2.pdf	https://github.com/huyng/incertae	false	false	true	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/allenai/allennlp-models	false	false	false	pytorch
https://paperswithcode.com/paper/pfld-a-practical-facial-landmark-detector	PFLD: A Practical Facial Landmark Detector	1902.10859	http://arxiv.org/abs/1902.10859v2	http://arxiv.org/pdf/1902.10859v2.pdf	https://github.com/Ontheway361/pfld-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/trackerbots-software-in-the-loop-study-of	TrackerBots: Software in the Loop Study of Quad-Copter Robots for Locating Radio-tags in a 3D Space	null	https://ssl.linklings.net/conferences/acra/acra2018_proceedings/views/by_sub_type.html	https://ssl.linklings.net/conferences/acra/acra2018_proceedings/views/includes/files/pap118s1-file1.pdf	https://github.com/AdelaideAuto-IDLab/TrackerBots	false	true	false	none
https://paperswithcode.com/paper/polysemy-deciphering-network-for-robust-human	Polysemy Deciphering Network for Robust Human-Object Interaction Detection	2008.02918	https://arxiv.org/abs/2008.02918v3	https://arxiv.org/pdf/2008.02918v3.pdf	https://github.com/MuchHair/PD-Net	true	true	true	none
https://paperswithcode.com/paper/a-local-block-coordinate-descent-algorithm-1	A Local Block Coordinate Descent Algorithm for the CSC Model	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Zisselman_A_Local_Block_Coordinate_Descent_Algorithm_for_the_CSC_Model_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Zisselman_A_Local_Block_Coordinate_Descent_Algorithm_for_the_CSC_Model_CVPR_2019_paper.pdf	https://github.com/EvZissel/LoBCoD	true	true	false	none
https://paperswithcode.com/paper/predicting-slice-to-volume-transformation-in	Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion	1702.08891	http://arxiv.org/abs/1702.08891v2	http://arxiv.org/pdf/1702.08891v2.pdf	https://github.com/farrell236/SVRnet	false	false	true	tf
https://paperswithcode.com/paper/computing-cnn-loss-and-gradients-for-pose	Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry	1805.01026	http://arxiv.org/abs/1805.01026v3	http://arxiv.org/pdf/1805.01026v3.pdf	https://github.com/farrell236/SVRnet	false	false	true	tf
https://paperswithcode.com/paper/the-implicit-bias-of-depth-how-incremental	The Implicit Bias of Depth: How Incremental Learning Drives Generalization	1909.12051	https://arxiv.org/abs/1909.12051v2	https://arxiv.org/pdf/1909.12051v2.pdf	https://github.com/dsgissin/Incremental-Learning	true	true	false	tf
https://paperswithcode.com/paper/classifying-idiomatic-and-literal-expressions-2	Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions	1802.09961	http://arxiv.org/abs/1802.09961v1	http://arxiv.org/pdf/1802.09961v1.pdf	https://github.com/bondfeld/BNC_idioms	false	false	true	none
https://paperswithcode.com/paper/temporal-generative-adversarial-nets-with	Temporal Generative Adversarial Nets with Singular Value Clipping	1611.06624	http://arxiv.org/abs/1611.06624v3	http://arxiv.org/pdf/1611.06624v3.pdf	https://github.com/pfnet-research/tgan	false	false	true	none
https://paperswithcode.com/paper/pcpnet-learning-local-shape-properties-from	PCPNET: Learning Local Shape Properties from Raw Point Clouds	1710.04954	http://arxiv.org/abs/1710.04954v4	http://arxiv.org/pdf/1710.04954v4.pdf	https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds	false	false	true	tf
https://paperswithcode.com/paper/bayesian-estimation-of-a-semiparametric	Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni Syndrome	1804.06883	http://arxiv.org/abs/1804.06883v1	http://arxiv.org/pdf/1804.06883v1.pdf	https://github.com/wwylab/MPC	true	false	true	none
https://paperswithcode.com/paper/aesthetic-driven-image-enhancement-by	Aesthetic-Driven Image Enhancement by Adversarial Learning	1707.05251	http://arxiv.org/abs/1707.05251v2	http://arxiv.org/pdf/1707.05251v2.pdf	https://github.com/dannysdeng/EnhanceGAN	false	false	true	torch
https://paperswithcode.com/paper/sampling-errors-in-nested-sampling-parameter	Sampling Errors in Nested Sampling Parameter Estimation	1703.09701	http://arxiv.org/abs/1703.09701v2	http://arxiv.org/pdf/1703.09701v2.pdf	https://github.com/ejhigson/perfectns	false	false	true	none
https://paperswithcode.com/paper/neural-discrete-representation-learning	Neural Discrete Representation Learning	1711.00937	http://arxiv.org/abs/1711.00937v2	http://arxiv.org/pdf/1711.00937v2.pdf	https://github.com/keras-team/keras-io/blob/master/examples/generative/vq_vae.py	false	false	false	tf
https://paperswithcode.com/paper/all-word-embeddings-from-one-embedding	All Word Embeddings from One Embedding	2004.12073	https://arxiv.org/abs/2004.12073v3	https://arxiv.org/pdf/2004.12073v3.pdf	https://github.com/takase/alone_seq2seq	true	true	true	pytorch
https://paperswithcode.com/paper/automatic-chemical-design-using-a-data-driven	Automatic chemical design using a data-driven continuous representation of molecules	1610.02415	http://arxiv.org/abs/1610.02415v3	http://arxiv.org/pdf/1610.02415v3.pdf	https://github.com/aspuru-guzik-group/chemical_vae	true	false	true	tf
https://paperswithcode.com/paper/uncertainty-estimates-and-multi-hypotheses	Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow	1802.07095	http://arxiv.org/abs/1802.07095v4	http://arxiv.org/pdf/1802.07095v4.pdf	https://github.com/lmb-freiburg/netdef-docker	false	false	true	tf
https://paperswithcode.com/paper/the-kinetics-human-action-video-dataset	The Kinetics Human Action Video Dataset	1705.06950	http://arxiv.org/abs/1705.06950v1	http://arxiv.org/pdf/1705.06950v1.pdf	https://github.com/deepmind/kinetics-i3d	false	false	true	tf
https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence	Convolutional Neural Networks for Sentence Classification	1408.5882	http://arxiv.org/abs/1408.5882v2	http://arxiv.org/pdf/1408.5882v2.pdf	https://github.com/piyush2896/CNN-Text-Classifier	false	false	true	tf
https://paperswithcode.com/paper/capsdemm-capsule-network-for-detection-of	CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images	1808.06428	http://arxiv.org/abs/1808.06428v2	http://arxiv.org/pdf/1808.06428v2.pdf	https://github.com/Anabik/CapsDeMM	true	true	false	tf
https://paperswithcode.com/paper/stgat-modeling-spatial-temporal-interactions	STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Huang_STGAT_Modeling_Spatial-Temporal_Interactions_for_Human_Trajectory_Prediction_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_STGAT_Modeling_Spatial-Temporal_Interactions_for_Human_Trajectory_Prediction_ICCV_2019_paper.pdf	https://github.com/huang-xx/STGAT	false	false	false	pytorch
https://paperswithcode.com/paper/multi-agent-active-perception-with-prediction	Multi-agent active perception with prediction rewards	2010.11835	https://arxiv.org/abs/2010.11835v1	https://arxiv.org/pdf/2010.11835v1.pdf	https://github.com/laurimi/multiagent-prediction-reward	true	true	true	none
https://paperswithcode.com/paper/singing-voice-separation-using-a-deep	Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy	1812.01278	http://arxiv.org/abs/1812.01278v1	http://arxiv.org/pdf/1812.01278v1.pdf	https://github.com/EdwardLin2014/CNN-with-IBM-for-Singing-Voice-Separation	false	false	true	tf
https://paperswithcode.com/paper/catalog-of-quasars-from-the-kilo-degree	Catalog of quasars from the Kilo-Degree Survey Data Release 3	1812.03084	http://arxiv.org/abs/1812.03084v2	http://arxiv.org/pdf/1812.03084v2.pdf	https://github.com/snakoneczny/kids-quasars	true	true	false	tf
https://paperswithcode.com/paper/pointnet-deep-learning-on-point-sets-for-3d	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation	1612.00593	http://arxiv.org/abs/1612.00593v2	http://arxiv.org/pdf/1612.00593v2.pdf	https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds	false	false	true	tf
https://paperswithcode.com/paper/q-map-a-convolutional-approach-for-goal	Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks	1810.02927	https://arxiv.org/abs/1810.02927v2	https://arxiv.org/pdf/1810.02927v2.pdf	https://github.com/yl3829/Q-map	false	false	true	tf
https://paperswithcode.com/paper/controlling-perceptual-factors-in-neural	Controlling Perceptual Factors in Neural Style Transfer	1611.07865	http://arxiv.org/abs/1611.07865v2	http://arxiv.org/pdf/1611.07865v2.pdf	https://github.com/dstein64/pastiche	false	false	true	pytorch
https://paperswithcode.com/paper/spatially-localized-atlas-network-tiles	Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data	1806.00546	http://arxiv.org/abs/1806.00546v2	http://arxiv.org/pdf/1806.00546v2.pdf	https://github.com/MASILab/SLANTbrainSeg	true	true	true	caffe2
https://paperswithcode.com/paper/3d-whole-brain-segmentation-using-spatially	3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles	1903.12152	http://arxiv.org/abs/1903.12152v1	http://arxiv.org/pdf/1903.12152v1.pdf	https://github.com/MASILab/SLANTbrainSeg	true	true	true	caffe2
https://paperswithcode.com/paper/stackgan-realistic-image-synthesis-with	StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks	1710.10916	http://arxiv.org/abs/1710.10916v3	http://arxiv.org/pdf/1710.10916v3.pdf	https://github.com/hanzhanggit/StackGAN-v2	true	true	true	pytorch
https://paperswithcode.com/paper/position-detection-and-direction-prediction	Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network	1806.04828	http://arxiv.org/abs/1806.04828v2	http://arxiv.org/pdf/1806.04828v2.pdf	https://github.com/DetectionTeamUCAS/RRPN_Faster_RCNN_Tensorflow	false	false	true	tf
https://paperswithcode.com/paper/learning-uncertainty-with-artificial-neural	Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes	2105.05559	https://arxiv.org/abs/2105.05559v1	https://arxiv.org/pdf/2105.05559v1.pdf	https://github.com/hansweytjens/uncertainty-remaining_time	true	true	false	pytorch
https://paperswithcode.com/paper/batch-stationary-distribution-estimation	Batch Stationary Distribution Estimation	2003.00722	https://arxiv.org/abs/2003.00722v1	https://arxiv.org/pdf/2003.00722v1.pdf	https://github.com/bmazoure/batch_stationary_distribution	false	false	true	pytorch
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	1810.04805	https://arxiv.org/abs/1810.04805v2	https://arxiv.org/pdf/1810.04805v2.pdf	https://github.com/IBM/MAX-Text-Sentiment-Classifier	false	false	true	tf
https://paperswithcode.com/paper/not-all-claims-are-created-equal-choosing-the	Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses	1911.03850	https://arxiv.org/abs/1911.03850v3	https://arxiv.org/pdf/1911.03850v3.pdf	https://github.com/allenai/HyBayes	true	true	false	none
https://paperswithcode.com/paper/on-cognitive-preferences-and-the-plausibility	On Cognitive Preferences and the Plausibility of Rule-based Models	1803.01316	http://arxiv.org/abs/1803.01316v4	http://arxiv.org/pdf/1803.01316v4.pdf	https://github.com/kliegr/rule-length-project	true	true	false	none
https://paperswithcode.com/paper/bottom-up-and-top-down-attention-for-image	Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	1707.07998	http://arxiv.org/abs/1707.07998v3	http://arxiv.org/pdf/1707.07998v3.pdf	https://github.com/BigRedT/info-ground	false	false	true	pytorch
https://paperswithcode.com/paper/a-regularized-framework-for-sparse-and	A Regularized Framework for Sparse and Structured Neural Attention	1705.07704	http://arxiv.org/abs/1705.07704v3	http://arxiv.org/pdf/1705.07704v3.pdf	https://github.com/vene/sparse-structured-attention	false	false	true	pytorch
https://paperswithcode.com/paper/generative-moment-matching-networks	Generative Moment Matching Networks	1502.02761	http://arxiv.org/abs/1502.02761v1	http://arxiv.org/pdf/1502.02761v1.pdf	https://github.com/Abhipanda4/GMMN-Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/ned-an-inter-graph-node-metric-based-on-edit	NED: An Inter-Graph Node Metric Based On Edit Distance	1602.02358	http://arxiv.org/abs/1602.02358v3	http://arxiv.org/pdf/1602.02358v3.pdf	https://github.com/zhuhaohan/NED	false	false	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/candy4869/2014	false	false	true	none
https://paperswithcode.com/paper/instance-aware-image-to-image-translation	Instance-aware Image-to-Image Translation	null	https://openreview.net/forum?id=ryxwJhC9YX	https://openreview.net/pdf?id=ryxwJhC9YX	https://github.com/sangwoomo/instagan	true	true	false	pytorch
https://paperswithcode.com/paper/mixup-beyond-empirical-risk-minimization	mixup: Beyond Empirical Risk Minimization	1710.09412	http://arxiv.org/abs/1710.09412v2	http://arxiv.org/pdf/1710.09412v2.pdf	https://github.com/transcendentsky/mixup	false	false	true	pytorch
https://paperswithcode.com/paper/unsupervised-feature-selection-with-adaptive	Unsupervised Feature Selection with Adaptive Structure Learning	1504.00736	http://arxiv.org/abs/1504.00736v1	http://arxiv.org/pdf/1504.00736v1.pdf	https://github.com/csliangdu/FSASL	true	true	false	none
https://paperswithcode.com/paper/deep-reinforcement-learning-for-dialogue	Deep Reinforcement Learning for Dialogue Generation	1606.01541	http://arxiv.org/abs/1606.01541v4	http://arxiv.org/pdf/1606.01541v4.pdf	https://github.com/tfolkman/deep-learning-experiments	false	false	true	pytorch
https://paperswithcode.com/paper/deep-architectures-for-learning-context	Deep Architectures for Learning Context-dependent Ranking Functions	1803.05796	http://arxiv.org/abs/1803.05796v2	http://arxiv.org/pdf/1803.05796v2.pdf	https://github.com/kiudee/cs-ranking	true	true	true	tf
https://paperswithcode.com/paper/training-big-random-forests-with-little	Training Big Random Forests with Little Resources	1802.06394	http://arxiv.org/abs/1802.06394v1	http://arxiv.org/pdf/1802.06394v1.pdf	https://github.com/gieseke/woody	true	true	false	none
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks	1703.10593	https://arxiv.org/abs/1703.10593v7	https://arxiv.org/pdf/1703.10593v7.pdf	https://github.com/dgedon/CycleGAN-for-Emojis	false	false	false	pytorch
https://paperswithcode.com/paper/cyclegan-face-off	CycleGAN Face-off	1712.03451	http://arxiv.org/abs/1712.03451v5	http://arxiv.org/pdf/1712.03451v5.pdf	https://github.com/ShangxuanWu/CycleGAN-Face-off	true	true	true	pytorch
https://paperswithcode.com/paper/less-is-more-sampling-chemical-space-with	Less is more: sampling chemical space with active learning	1801.09319	http://arxiv.org/abs/1801.09319v2	http://arxiv.org/pdf/1801.09319v2.pdf	https://github.com/isayev/ANI1_dataset	false	false	true	none
https://paperswithcode.com/paper/learning-where-to-attend-like-a-human-driver	Learning Where to Attend Like a Human Driver	1611.08215	http://arxiv.org/abs/1611.08215v2	http://arxiv.org/pdf/1611.08215v2.pdf	https://github.com/francescosolera/dreyeving	true	true	false	none
https://paperswithcode.com/paper/dense-distributions-from-sparse-samples	Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA	1505.02065	http://arxiv.org/abs/1505.02065v6	http://arxiv.org/pdf/1505.02065v6.pdf	https://github.com/ypapanik/cgs_p	true	true	false	none
https://paperswithcode.com/paper/resnest-split-attention-networks	ResNeSt: Split-Attention Networks	2004.08955	https://arxiv.org/abs/2004.08955v2	https://arxiv.org/pdf/2004.08955v2.pdf	https://github.com/chongruo/detectron2-resnest	false	false	true	pytorch
https://paperswithcode.com/paper/replication-issues-in-syntax-based-aspect	Replication issues in syntax-based aspect extraction for opinion mining	1701.01565	http://arxiv.org/abs/1701.01565v1	http://arxiv.org/pdf/1701.01565v1.pdf	https://github.com/epochx/opminreplicability	true	true	false	none
https://paperswithcode.com/paper/disentangling-noise-from-images-a-flow-based	Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network	2105.04746	https://arxiv.org/abs/2105.04746v1	https://arxiv.org/pdf/2105.04746v1.pdf	https://github.com/Yang-Liu1082/FDN	true	true	false	none
https://paperswithcode.com/paper/adaptive-graph-convolutional-recurrent	Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting	2007.02842	https://arxiv.org/abs/2007.02842v2	https://arxiv.org/pdf/2007.02842v2.pdf	https://github.com/LeiBAI/AGCRN	true	true	true	pytorch
https://paperswithcode.com/paper/variational-neural-machine-translation	Variational Neural Machine Translation	1605.07869	http://arxiv.org/abs/1605.07869v2	http://arxiv.org/pdf/1605.07869v2.pdf	https://github.com/DeepLearnXMU/VNMT	true	true	false	none
https://paperswithcode.com/paper/improved-network-robustness-with-adversary	Improved Network Robustness with Adversary Critic	1810.12576	http://arxiv.org/abs/1810.12576v1	http://arxiv.org/pdf/1810.12576v1.pdf	https://github.com/aam-at/adversary_critic	true	true	false	tf
https://paperswithcode.com/paper/parsimonious-topic-models-with-salient-word	Parsimonious Topic Models with Salient Word Discovery	1401.6169	http://arxiv.org/abs/1401.6169v2	http://arxiv.org/pdf/1401.6169v2.pdf	https://github.com/hsoleimani/PTM	true	true	false	none
https://paperswithcode.com/paper/kernel-adaptive-metropolis-hastings	Kernel Adaptive Metropolis-Hastings	1307.5302	http://arxiv.org/abs/1307.5302v3	http://arxiv.org/pdf/1307.5302v3.pdf	https://github.com/karlnapf/kameleon-mcmc	true	true	false	none
https://paperswithcode.com/paper/intent-based-radio-scheduler-for-ran-slicing-1	Intent-based Radio Scheduler for RAN Slicing: Learning to deal with different network scenarios	2501.00950	https://arxiv.org/abs/2501.00950v1	https://arxiv.org/pdf/2501.00950v1.pdf	https://github.com/lasseufpa/intent_radio_sched_multi_slice	true	false	true	none
https://paperswithcode.com/paper/improved-training-of-wasserstein-gans	Improved Training of Wasserstein GANs	1704.00028	http://arxiv.org/abs/1704.00028v3	http://arxiv.org/pdf/1704.00028v3.pdf	https://github.com/adler-j/bwgan	false	false	true	tf
https://paperswithcode.com/paper/ssgd-a-safe-and-efficient-method-of-gradient	SSGD: A safe and efficient method of gradient descent	2012.02076	https://arxiv.org/abs/2012.02076v2	https://arxiv.org/pdf/2012.02076v2.pdf	https://github.com/duanjinhuan/SSGD	true	true	false	none
https://paperswithcode.com/paper/finding-tiny-faces	Finding Tiny Faces	1612.04402	http://arxiv.org/abs/1612.04402v2	http://arxiv.org/pdf/1612.04402v2.pdf	https://github.com/cydonia999/Tiny_Faces_in_Tensorflow	false	false	true	tf
https://paperswithcode.com/paper/esrgan-enhanced-super-resolution-generative	ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks	1809.00219	http://arxiv.org/abs/1809.00219v2	http://arxiv.org/pdf/1809.00219v2.pdf	https://github.com/u7javed/Image-Enhancer-via-ESRGAN	false	false	true	pytorch
https://paperswithcode.com/paper/differentiable-quantum-architecture-search	Differentiable Quantum Architecture Search	2010.08561	https://arxiv.org/abs/2010.08561v2	https://arxiv.org/pdf/2010.08561v2.pdf	https://github.com/refraction-ray/tensorcircuit	true	true	true	tf
https://paperswithcode.com/paper/rectangular-statistical-cartograms-in-r-the	Rectangular Statistical Cartograms in R: The recmap Package	1606.00464	http://arxiv.org/abs/1606.00464v2	http://arxiv.org/pdf/1606.00464v2.pdf	https://github.com/cpanse/recmap	true	false	true	none
https://paperswithcode.com/paper/proofwatch-watchlist-guidance-for-large	ProofWatch: Watchlist Guidance for Large Theories in E	1802.04007	http://arxiv.org/abs/1802.04007v2	http://arxiv.org/pdf/1802.04007v2.pdf	https://github.com/ai4reason/eprover-data	true	true	false	none
https://paperswithcode.com/paper/attention-is-all-you-need	Attention Is All You Need	1706.03762	https://arxiv.org/abs/1706.03762v7	https://arxiv.org/pdf/1706.03762v7.pdf	https://github.com/kolloldas/torchnlp	false	false	true	pytorch
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/cdesilv1/sc2_ai_cdes	false	false	true	tf
https://paperswithcode.com/paper/efficient-statistical-classification-of	Efficient statistical classification of satellite measurements	1202.2194	http://arxiv.org/abs/1202.2194v4	http://arxiv.org/pdf/1202.2194v4.pdf	https://github.com/tommyod/KDEpy	false	false	true	none
https://paperswithcode.com/paper/samplernn-an-unconditional-end-to-end-neural	SampleRNN: An Unconditional End-to-End Neural Audio Generation Model	1612.07837	http://arxiv.org/abs/1612.07837v2	http://arxiv.org/pdf/1612.07837v2.pdf	https://github.com/dada-bots/dadabots_sampleRNN	false	false	false	none
https://paperswithcode.com/paper/a-framework-for-evaluation-of-composite	A Framework for Evaluation of Composite Memento Temporal Coherence	1402.0928	http://arxiv.org/abs/1402.0928v3	http://arxiv.org/pdf/1402.0928v3.pdf	https://github.com/wikimedia/mediawiki-extensions-Memento	false	false	true	none
https://paperswithcode.com/paper/adversarial-feature-learning	Adversarial Feature Learning	1605.09782	http://arxiv.org/abs/1605.09782v7	http://arxiv.org/pdf/1605.09782v7.pdf	https://github.com/jeffdonahue/bigan	false	false	true	none

Subsets and Splits

Framework Repo Connectivity Analysis

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

Deduplicated Paper-Code Links

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

Paper Repo Counts & Distribution

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

Quantum Papers with Code Links

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

Financial Stock Price Prediction

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

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.