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train · 300k rows

paper_url stringlengths 36 81	paper_title stringlengths 1 242 ⌀	paper_arxiv_id stringlengths 9 16 ⌀	paper_url_abs stringlengths 18 314	paper_url_pdf stringlengths 21 935 ⌀	repo_url stringlengths 26 200	is_official bool 2 classes	mentioned_in_paper bool 2 classes	mentioned_in_github bool 2 classes	framework stringclasses 9 values
https://paperswithcode.com/paper/deep-learning-models-for-multilingual-hate	Deep Learning Models for Multilingual Hate Speech Detection	2004.06465	https://arxiv.org/abs/2004.06465v3	https://arxiv.org/pdf/2004.06465v3.pdf	https://github.com/punyajoy/DE-LIMIT	true	true	true	pytorch
https://paperswithcode.com/paper/self-supervised-graph-learning-with	Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction	2106.04751	https://arxiv.org/abs/2106.04751v2	https://arxiv.org/pdf/2106.04751v2.pdf	https://github.com/LuChang-CS/sherbet	true	true	false	tf
https://paperswithcode.com/paper/helping-results-assessment-by-adding	Helping results assessment by adding explainable elements to the deep relevance matching model	2106.05147	https://arxiv.org/abs/2106.05147v1	https://arxiv.org/pdf/2106.05147v1.pdf	https://github.com/giannisosx/explainable-search-drmm	true	true	false	none
https://paperswithcode.com/paper/dags-with-no-curl-an-efficient-dag-structure	DAGs with No Curl: An Efficient DAG Structure Learning Approach	2106.07197	https://arxiv.org/abs/2106.07197v1	https://arxiv.org/pdf/2106.07197v1.pdf	https://github.com/fishmoon1234/DAG-NoCurl	true	true	false	pytorch
https://paperswithcode.com/paper/graph-neural-network-based-anomaly-detection	Graph Neural Network-Based Anomaly Detection in Multivariate Time Series	2106.06947	https://arxiv.org/abs/2106.06947v1	https://arxiv.org/pdf/2106.06947v1.pdf	https://github.com/d-ailin/GDN	true	true	true	pytorch
https://paperswithcode.com/paper/weakly-supervised-high-resolution	Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis	2106.07049	https://arxiv.org/abs/2106.07049v2	https://arxiv.org/pdf/2106.07049v2.pdf	https://github.com/nyukat/GLAM	true	true	false	pytorch
https://paperswithcode.com/paper/knowledge-embedded-routing-network-for-scene	Knowledge-Embedded Routing Network for Scene Graph Generation	1903.03326	http://arxiv.org/abs/1903.03326v1	http://arxiv.org/pdf/1903.03326v1.pdf	https://github.com/HCPLab-SYSU/KERN	false	false	true	pytorch
https://paperswithcode.com/paper/dynamics-and-sensitivity-of-signaling	Dynamics and Sensitivity of Signaling Pathways	2106.06929	https://arxiv.org/abs/2106.06929v1	https://arxiv.org/pdf/2106.06929v1.pdf	https://github.com/sys-bio/CodeForPublishedPapers	true	true	false	none
https://paperswithcode.com/paper/triangle-sides-for-congruent-numbers-less	Triangle Sides for Congruent Numbers less than 10,000	2106.07373	https://arxiv.org/abs/2106.07373v1	https://arxiv.org/pdf/2106.07373v1.pdf	https://github.com/dgpaloalto/Congruent-Numbers	true	true	false	none
https://paperswithcode.com/paper/a-practical-introduction-to-regression	A Practical Introduction to Regression Discontinuity Designs: Foundations	1911.09511	https://arxiv.org/abs/1911.09511v1	https://arxiv.org/pdf/1911.09511v1.pdf	https://github.com/rdpackages-replication/CIT_2019_CUP	true	false	false	none
https://paperswithcode.com/paper/embedding-transfer-with-label-relaxation-for	Embedding Transfer with Label Relaxation for Improved Metric Learning	2103.14908	https://arxiv.org/abs/2103.14908v1	https://arxiv.org/pdf/2103.14908v1.pdf	https://github.com/tjddus9597/LabelRelaxation-CVPR21	false	false	true	pytorch
https://paperswithcode.com/paper/nprobust-nonparametric-kernel-based	nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference	1906.00198	https://arxiv.org/abs/1906.00198v1	https://arxiv.org/pdf/1906.00198v1.pdf	https://github.com/nppackages/nprobust	true	false	false	none
https://paperswithcode.com/paper/towards-better-exploiting-convolutional	Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification	1602.01517	http://arxiv.org/abs/1602.01517v1	http://arxiv.org/pdf/1602.01517v1.pdf	https://github.com/keillernogueira/exploit-cnn-rs	true	false	false	caffe2
https://paperswithcode.com/paper/wnut-2020-task-2-identification-of	WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets	2010.08232	https://arxiv.org/abs/2010.08232v1	https://arxiv.org/pdf/2010.08232v1.pdf	https://github.com/VinAIResearch/COVID19Tweet	false	false	false	none
https://paperswithcode.com/paper/skin-lesion-analysis-toward-melanoma	Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)	1710.05006	http://arxiv.org/abs/1710.05006v3	http://arxiv.org/pdf/1710.05006v3.pdf	https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification	false	false	true	tf
https://paperswithcode.com/paper/pseudo-healthy-synthesis-with-pathology	Pseudo-healthy synthesis with pathology disentanglement and adversarial learning	2005.01607	https://arxiv.org/abs/2005.01607v3	https://arxiv.org/pdf/2005.01607v3.pdf	https://github.com/xiat0616/pseudo-healthy-synthesis	true	true	false	none
https://paperswithcode.com/paper/single-uhd-image-dehazing-via-interpretable	Single UHD Image Dehazing via Interpretable Pyramid Network	2202.08589	https://arxiv.org/abs/2202.08589v1	https://arxiv.org/pdf/2202.08589v1.pdf	https://github.com/zzr-idam/4KDehazing	false	false	true	pytorch
https://paperswithcode.com/paper/draw-a-recurrent-neural-network-for-image	DRAW: A Recurrent Neural Network For Image Generation	1502.04623	http://arxiv.org/abs/1502.04623v2	http://arxiv.org/pdf/1502.04623v2.pdf	https://github.com/simonamtoft/ml-library	false	false	true	pytorch
https://paperswithcode.com/paper/an-introduction-to-variational-autoencoders	An Introduction to Variational Autoencoders	1906.02691	https://arxiv.org/abs/1906.02691v3	https://arxiv.org/pdf/1906.02691v3.pdf	https://github.com/simonamtoft/ml-library	false	false	true	pytorch
https://paperswithcode.com/paper/tutorial-on-variational-autoencoders	Tutorial on Variational Autoencoders	1606.05908	https://arxiv.org/abs/1606.05908v3	https://arxiv.org/pdf/1606.05908v3.pdf	https://github.com/simonamtoft/ml-library	false	false	true	pytorch
https://paperswithcode.com/paper/sqil-imitation-learning-via-regularized	SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards	1905.11108	https://arxiv.org/abs/1905.11108v3	https://arxiv.org/pdf/1905.11108v3.pdf	https://github.com/Div99/IQ-Learn	false	false	false	pytorch
https://paperswithcode.com/paper/deepgreen-deep-learning-of-green-s-functions	DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems	2101.07206	https://arxiv.org/abs/2101.07206v1	https://arxiv.org/pdf/2101.07206v1.pdf	https://github.com/sheadan/DeepGreen	true	false	true	tf
https://paperswithcode.com/paper/versavis-an-open-versatile-multi-camera	VersaVIS: An Open Versatile Multi-Camera Visual-Inertial Sensor Suite	1912.02469	https://arxiv.org/abs/1912.02469v1	https://arxiv.org/pdf/1912.02469v1.pdf	https://github.com/rikba/versavis	false	false	true	none
https://paperswithcode.com/paper/one-to-many-approach-for-improving-super	One-to-many Approach for Improving Super-Resolution	2106.10437	https://arxiv.org/abs/2106.10437v4	https://arxiv.org/pdf/2106.10437v4.pdf	https://github.com/krenerd/ultimate-sr	true	true	true	tf
https://paperswithcode.com/paper/vimpac-video-pre-training-via-masked-token	VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning	2106.11250	https://arxiv.org/abs/2106.11250v1	https://arxiv.org/pdf/2106.11250v1.pdf	https://github.com/airsplay/vimpac	true	true	true	pytorch
https://paperswithcode.com/paper/tight-approximate-differential-privacy-for	Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT	2006.07134	https://arxiv.org/abs/2006.07134v3	https://arxiv.org/pdf/2006.07134v3.pdf	https://github.com/DPBayes/PLD-Accountant	true	true	false	none
https://paperswithcode.com/paper/statistical-inference-of-the-value-function	Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings	2001.04515	https://arxiv.org/abs/2001.04515v2	https://arxiv.org/pdf/2001.04515v2.pdf	https://github.com/shengzhang37/SAVE	true	true	false	none
https://paperswithcode.com/paper/on-the-unreasonable-effectiveness-of-1	On the Unreasonable Effectiveness of Centroids in Image Retrieval	2104.13643	https://arxiv.org/abs/2104.13643v1	https://arxiv.org/pdf/2104.13643v1.pdf	https://github.com/lannguyen0910/deep-efficient-person-reid	false	false	true	pytorch
https://paperswithcode.com/paper/nutrition5k-towards-automatic-nutritional	Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food	2103.03375	https://arxiv.org/abs/2103.03375v2	https://arxiv.org/pdf/2103.03375v2.pdf	https://github.com/google-research-datasets/Nutrition5k	true	true	true	tf
https://paperswithcode.com/paper/learn-to-resolve-conversational-dependency-a	Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering	2106.11575	https://arxiv.org/abs/2106.11575v1	https://arxiv.org/pdf/2106.11575v1.pdf	https://github.com/dmis-lab/excord	true	true	false	pytorch
https://paperswithcode.com/paper/a-distributional-perspective-on-reinforcement	A Distributional Perspective on Reinforcement Learning	1707.06887	http://arxiv.org/abs/1707.06887v1	http://arxiv.org/pdf/1707.06887v1.pdf	https://github.com/qgallouedec/deep_rl	false	false	false	pytorch
https://paperswithcode.com/paper/zero-shot-chinese-character-recognition-with	Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition	2106.11613	https://arxiv.org/abs/2106.11613v1	https://arxiv.org/pdf/2106.11613v1.pdf	https://github.com/FudanVI/FudanOCR/tree/main/stroke-level-decomposition	true	false	false	pytorch
https://paperswithcode.com/paper/towards-knowledge-grounded-counter-narrative	Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech	2106.11783	https://arxiv.org/abs/2106.11783v1	https://arxiv.org/pdf/2106.11783v1.pdf	https://github.com/marcoguerini/CONAN	true	true	false	none
https://paperswithcode.com/paper/inspiration-through-observation-demonstrating	Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative Writing	2107.04007	https://arxiv.org/abs/2107.04007v1	https://arxiv.org/pdf/2107.04007v1.pdf	https://github.com/roemmele/InSentive	true	true	false	none
https://paperswithcode.com/paper/lightweight-robust-size-aware-cache	Lightweight Robust Size Aware Cache Management	2105.08770	https://arxiv.org/abs/2105.08770v2	https://arxiv.org/pdf/2105.08770v2.pdf	https://github.com/ohadeytan/caffeine	true	true	true	none
https://paperswithcode.com/paper/behavior-sequence-transformer-for-e-commerce	Behavior Sequence Transformer for E-commerce Recommendation in Alibaba	1905.06874	https://arxiv.org/abs/1905.06874v1	https://arxiv.org/pdf/1905.06874v1.pdf	https://github.com/jiwidi/Behavior-Sequence-Transformer-Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/atari-5-distilling-the-arcade-learning	Atari-5: Distilling the Arcade Learning Environment down to Five Games	2210.02019	https://arxiv.org/abs/2210.02019v1	https://arxiv.org/pdf/2210.02019v1.pdf	https://github.com/maitchison/atari-5	true	true	false	none
https://paperswithcode.com/paper/can-word-sense-distribution-detect-semantic	Can Word Sense Distribution Detect Semantic Changes of Words?	2310.10400	https://arxiv.org/abs/2310.10400v1	https://arxiv.org/pdf/2310.10400v1.pdf	https://github.com/LivNLP/Sense-based-Semantic-Change-Prediction	true	true	false	pytorch
https://paperswithcode.com/paper/imitation-learning-via-off-policy-1	Imitation Learning via Off-Policy Distribution Matching	1912.05032	https://arxiv.org/abs/1912.05032v1	https://arxiv.org/pdf/1912.05032v1.pdf	https://github.com/Div99/IQ-Learn	false	false	false	pytorch
https://paperswithcode.com/paper/distinguishing-short-duration-noise	Distinguishing short duration noise transients in LIGO data to improve the PyCBC search for gravitational waves from high mass binary black hole mergers	1709.08974	http://arxiv.org/abs/1709.08974v2	http://arxiv.org/pdf/1709.08974v2.pdf	https://github.com/gwastro/1-ogc	false	false	true	none
https://paperswithcode.com/paper/solving-reward-collecting-problems-with-uavs	Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning	2112.00141	https://arxiv.org/abs/2112.00141v1	https://arxiv.org/pdf/2112.00141v1.pdf	https://github.com/benliu31492/solving-reward-collecting-problems-with-uavs-a-comparison-of-online-optimization-and-q-learning	true	true	false	tf
https://paperswithcode.com/paper/hierarchical-variational-memory-for-few-shot-1	Hierarchical Variational Memory for Few-shot Learning Across Domains	2112.08181	https://arxiv.org/abs/2112.08181v2	https://arxiv.org/pdf/2112.08181v2.pdf	https://github.com/ydu-uva/hiermemory	true	true	false	tf
https://paperswithcode.com/paper/unraveling-the-hidden-organisation-of-urban	Unraveling the hidden organisation of urban systems and their mobility flows	1908.02538	https://arxiv.org/abs/1908.02538v2	https://arxiv.org/pdf/1908.02538v2.pdf	https://github.com/gbertagnolli/intsegration	false	false	true	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/Dlut-lab-zmn/Image-Captioning-Attack	false	false	true	pytorch
https://paperswithcode.com/paper/self-critical-sequence-training-for-image	Self-critical Sequence Training for Image Captioning	1612.00563	http://arxiv.org/abs/1612.00563v2	http://arxiv.org/pdf/1612.00563v2.pdf	https://github.com/Dlut-lab-zmn/Image-Captioning-Attack	false	false	true	pytorch
https://paperswithcode.com/paper/efficiently-combining-human-demonstrations	Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time	1810.11545	http://arxiv.org/abs/1810.11545v2	http://arxiv.org/pdf/1810.11545v2.pdf	https://github.com/viniciusguigo/complete_col	false	false	true	tf
https://paperswithcode.com/paper/cycle-of-learning-for-autonomous-systems-from	Cycle-of-Learning for Autonomous Systems from Human Interaction	1808.09572	http://arxiv.org/abs/1808.09572v2	http://arxiv.org/pdf/1808.09572v2.pdf	https://github.com/viniciusguigo/complete_col	false	false	true	tf
https://paperswithcode.com/paper/data-clustering-and-noise-undressing-for	Data clustering and noise undressing for correlation matrices	cond-mat/0101237	https://arxiv.org/abs/cond-mat/0101237v1	https://arxiv.org/pdf/cond-mat/0101237v1.pdf	https://github.com/lyelibi/timeseries_gen	false	false	true	none
https://paperswithcode.com/paper/unireplknet-a-universal-perception-large-1	UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition	null	http://openaccess.thecvf.com//content/CVPR2024/html/Ding_UniRepLKNet_A_Universal_Perception_Large-Kernel_ConvNet_for_Audio_Video_Point_CVPR_2024_paper.html	http://openaccess.thecvf.com//content/CVPR2024/papers/Ding_UniRepLKNet_A_Universal_Perception_Large-Kernel_ConvNet_for_Audio_Video_Point_CVPR_2024_paper.pdf	https://github.com/ailab-cvc/unireplknet	true	true	false	pytorch
https://paperswithcode.com/paper/agglomerative-fast-super-paramagnetic	Agglomerative Likelihood Clustering	1908.00951	https://arxiv.org/abs/1908.00951v4	https://arxiv.org/pdf/1908.00951v4.pdf	https://github.com/lyelibi/timeseries_gen	true	true	true	none
https://paperswithcode.com/paper/learning-long-horizon-robot-exploration	Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces	2205.11384	https://arxiv.org/abs/2205.11384v2	https://arxiv.org/pdf/2205.11384v2.pdf	https://github.com/robot-learning-freiburg/Multi-Object-Search	true	false	false	pytorch
https://paperswithcode.com/paper/probing-the-robustness-of-trained-metrics-for-1	Probing the Robustness of Trained Metrics for Conversational Dialogue Systems	2202.13887	https://arxiv.org/abs/2202.13887v1	https://arxiv.org/pdf/2202.13887v1.pdf	https://github.com/jderiu/metric-robustness	true	false	true	pytorch
https://paperswithcode.com/paper/learning-deep-features-for-discriminative	Learning Deep Features for Discriminative Localization	1512.04150	http://arxiv.org/abs/1512.04150v1	http://arxiv.org/pdf/1512.04150v1.pdf	https://github.com/Azure/AzureChestXRay	false	false	true	pytorch
https://paperswithcode.com/paper/exhaustive-symbolic-regression	Exhaustive Symbolic Regression	2211.11461	https://arxiv.org/abs/2211.11461v2	https://arxiv.org/pdf/2211.11461v2.pdf	https://github.com/MindSpore-MS-Code2/code0/tree/main/esr_ea	false	false	false	mindspore
https://paperswithcode.com/paper/etpc-a-paraphrase-identification-corpus	ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation	null	https://aclanthology.org/L18-1221	https://aclanthology.org/L18-1221.pdf	https://github.com/venelink/ETPC	true	true	false	none
https://paperswithcode.com/paper/a-simple-pooling-based-design-for-real-time	A Simple Pooling-Based Design for Real-Time Salient Object Detection	1904.09569	http://arxiv.org/abs/1904.09569v1	http://arxiv.org/pdf/1904.09569v1.pdf	https://github.com/chouxianyu/Boundary-Aware-PoolNet	false	false	false	pytorch
https://paperswithcode.com/paper/basnet-boundary-aware-salient-object	BASNet: Boundary-Aware Salient Object Detection	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.pdf	https://github.com/chouxianyu/Boundary-Aware-PoolNet	false	false	false	pytorch
https://paperswithcode.com/paper/training-data-efficient-image-transformers	Training data-efficient image transformers & distillation through attention	2012.12877	https://arxiv.org/abs/2012.12877v2	https://arxiv.org/pdf/2012.12877v2.pdf	https://github.com/UdbhavPrasad072300/Transformer-Implementations	false	false	true	pytorch
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/UdbhavPrasad072300/Transformer-Implementations	false	false	true	pytorch
https://paperswithcode.com/paper/probabilistic-contrastive-principal-component	Probabilistic Contrastive Principal Component Analysis	2012.07977	https://arxiv.org/abs/2012.07977v2	https://arxiv.org/pdf/2012.07977v2.pdf	https://github.com/AllaVinner/PCPCA-a-little-look	false	false	true	none
https://paperswithcode.com/paper/a-free-viewpoint-portrait-generator-with	SofGAN: A Portrait Image Generator with Dynamic Styling	2007.03780	https://arxiv.org/abs/2007.03780v2	https://arxiv.org/pdf/2007.03780v2.pdf	https://github.com/apchenstu/sofgan	true	true	true	pytorch
https://paperswithcode.com/paper/silhouette-based-view-embeddings-for-gait	Silhouette based View embeddings for Gait Recognition under Multiple Views	2108.05524	https://arxiv.org/abs/2108.05524v1	https://arxiv.org/pdf/2108.05524v1.pdf	https://github.com/ctrasd/gait-view	true	true	false	pytorch
https://paperswithcode.com/paper/bag-of-tricks-for-long-tail-visual	Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera-Trap Images	2206.12458	https://arxiv.org/abs/2206.12458v3	https://arxiv.org/pdf/2206.12458v3.pdf	https://github.com/alcunha/bagoftricks4cameratraps	true	false	false	tf
https://paperswithcode.com/paper/black-box-safety-analysis-and-retraining-of	Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering	2201.05077	https://arxiv.org/abs/2201.05077v4	https://arxiv.org/pdf/2201.05077v4.pdf	https://zenodo.org/record/6619279	true	false	false	none
https://paperswithcode.com/paper/proximal-policy-optimization-algorithms	Proximal Policy Optimization Algorithms	1707.06347	http://arxiv.org/abs/1707.06347v2	http://arxiv.org/pdf/1707.06347v2.pdf	https://github.com/gaetanserre/l2rpn-2022_ppo-baseline	false	false	true	none
https://paperswithcode.com/paper/optimal-transport-kernels-for-sequential-and	Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search	2006.07593	https://arxiv.org/abs/2006.07593v3	https://arxiv.org/pdf/2006.07593v3.pdf	https://github.com/ntienvu/TW_NAS	true	true	true	pytorch
https://paperswithcode.com/paper/deep-historical-borrowing-framework-to	Deep Historical Borrowing Framework to Prospectively and Simultaneously Synthesize Control Information in Confirmatory Clinical Trials with Multiple Endpoints	2008.12774	https://arxiv.org/abs/2008.12774v2	https://arxiv.org/pdf/2008.12774v2.pdf	https://github.com/tian-yu-zhan/deep_historical_borrowing	true	true	false	none
https://paperswithcode.com/paper/data-distributional-properties-drive-emergent	Data Distributional Properties Drive Emergent In-Context Learning in Transformers	2205.05055	https://arxiv.org/abs/2205.05055v6	https://arxiv.org/pdf/2205.05055v6.pdf	https://github.com/deepmind/emergent_in_context_learning	true	true	true	jax
https://paperswithcode.com/paper/mask-r-cnn	Mask R-CNN	1703.06870	http://arxiv.org/abs/1703.06870v3	http://arxiv.org/pdf/1703.06870v3.pdf	https://github.com/houssemjebari/Fruit-Detection	false	false	true	none
https://paperswithcode.com/paper/fader-networks-manipulating-images-by-sliding	Fader Networks: Manipulating Images by Sliding Attributes	1706.00409	http://arxiv.org/abs/1706.00409v2	http://arxiv.org/pdf/1706.00409v2.pdf	https://github.com/sidwa/ae_thesis	false	false	true	pytorch
https://paperswithcode.com/paper/tutorial-on-variational-autoencoders	Tutorial on Variational Autoencoders	1606.05908	https://arxiv.org/abs/1606.05908v3	https://arxiv.org/pdf/1606.05908v3.pdf	https://github.com/sidwa/ae_thesis	false	false	true	pytorch
https://paperswithcode.com/paper/understanding-and-improving-interpolation-in	Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer	1807.07543	http://arxiv.org/abs/1807.07543v2	http://arxiv.org/pdf/1807.07543v2.pdf	https://github.com/sidwa/ae_thesis	false	false	true	pytorch
https://paperswithcode.com/paper/a-simple-framework-for-contrastive-learning	A Simple Framework for Contrastive Learning of Visual Representations	2002.05709	https://arxiv.org/abs/2002.05709v3	https://arxiv.org/pdf/2002.05709v3.pdf	https://github.com/sidwa/ae_thesis	false	false	true	pytorch
https://paperswithcode.com/paper/signgraph-a-sign-sequence-is-worth-graphs-of	SignGraph: A Sign Sequence is Worth Graphs of Nodes	null	http://openaccess.thecvf.com//content/CVPR2024/html/Gan_SignGraph_A_Sign_Sequence_is_Worth_Graphs_of_Nodes_CVPR_2024_paper.html	http://openaccess.thecvf.com//content/CVPR2024/papers/Gan_SignGraph_A_Sign_Sequence_is_Worth_Graphs_of_Nodes_CVPR_2024_paper.pdf	https://github.com/gswycf/signgraph	true	true	false	pytorch
https://paperswithcode.com/paper/erc-20r-and-erc-721r-reversible-transactions	ERC-20R and ERC-721R: Reversible Transactions on Ethereum	2208.00543	https://arxiv.org/abs/2208.00543v3	https://arxiv.org/pdf/2208.00543v3.pdf	https://github.com/kkailiwang/erc20r	true	true	true	none
https://paperswithcode.com/paper/ernie-enhanced-representation-through	ERNIE: Enhanced Representation through Knowledge Integration	1904.09223	http://arxiv.org/abs/1904.09223v1	http://arxiv.org/pdf/1904.09223v1.pdf	https://github.com/lyqcom/emotect	false	false	false	mindspore
https://paperswithcode.com/paper/what-and-how-well-you-performed-a-multitask	What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment	1904.04346	https://arxiv.org/abs/1904.04346v2	https://arxiv.org/pdf/1904.04346v2.pdf	https://github.com/InfoX-SEU/DAE-AQA	false	false	true	pytorch
https://paperswithcode.com/paper/a-biologically-plausible-neural-network-for	A biologically plausible neural network for multi-channel Canonical Correlation Analysis	2010.00525	https://arxiv.org/abs/2010.00525v4	https://arxiv.org/pdf/2010.00525v4.pdf	https://github.com/flatironinstitute/bio-cca	false	false	true	none
https://paperswithcode.com/paper/a-robust-consistent-information-criterion-for	A Robust Consistent Information Criterion for Model Selection based on Empirical Likelihood	2006.13281	http://arxiv.org/abs/2006.13281v1	http://arxiv.org/pdf/2006.13281v1.pdf	https://github.com/chencxxy28/ELCIC	false	false	true	none
https://paperswithcode.com/paper/combining-diverse-feature-priors-1	Combining Diverse Feature Priors	2110.08220	https://arxiv.org/abs/2110.08220v2	https://arxiv.org/pdf/2110.08220v2.pdf	https://github.com/MadryLab/copriors	true	true	true	pytorch
https://paperswithcode.com/paper/free-feature-refinement-for-generalized-zero	FREE: Feature Refinement for Generalized Zero-Shot Learning	2107.13807	https://arxiv.org/abs/2107.13807v1	https://arxiv.org/pdf/2107.13807v1.pdf	https://github.com/shiming-chen/FREE	true	true	true	pytorch
https://paperswithcode.com/paper/towards-layer-wise-image-vectorization-1	Towards Layer-wise Image Vectorization	2206.04655	https://arxiv.org/abs/2206.04655v1	https://arxiv.org/pdf/2206.04655v1.pdf	https://github.com/picsart-ai-research/live-layerwise-image-vectorization	true	true	true	pytorch
https://paperswithcode.com/paper/3dias-3d-shape-reconstruction-with-implicit	3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces	2108.08653	https://arxiv.org/abs/2108.08653v1	https://arxiv.org/pdf/2108.08653v1.pdf	https://github.com/myavartanoo/3DIAS_PyTorch	true	false	true	pytorch
https://paperswithcode.com/paper/steady-simultaneous-state-estimation-and	STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations	2203.01299	https://arxiv.org/abs/2203.01299v3	https://arxiv.org/pdf/2203.01299v3.pdf	https://github.com/mrvplusone/steady	true	true	true	none
https://paperswithcode.com/paper/learning-to-search-in-local-branching	Revisiting local branching with a machine learning lens	2112.02195	https://arxiv.org/abs/2112.02195v2	https://arxiv.org/pdf/2112.02195v2.pdf	https://github.com/pandat8/ml4lb	true	true	false	pytorch
https://paperswithcode.com/paper/kiu-net-overcomplete-convolutional	KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation	2010.01663	https://arxiv.org/abs/2010.01663v2	https://arxiv.org/pdf/2010.01663v2.pdf	https://github.com/jeya-maria-jose/KiU-Net-pytorch	true	true	true	pytorch
https://paperswithcode.com/paper/learning-delicate-local-representations-for	Learning Delicate Local Representations for Multi-Person Pose Estimation	2003.04030	https://arxiv.org/abs/2003.04030v3	https://arxiv.org/pdf/2003.04030v3.pdf	https://github.com/chenyilun95/tf-cpn	false	false	true	tf
https://paperswithcode.com/paper/directquote-a-dataset-for-direct-quotation	DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles	2110.07827	https://arxiv.org/abs/2110.07827v1	https://arxiv.org/pdf/2110.07827v1.pdf	https://github.com/thunlp-mt/directquote	true	true	false	none
https://paperswithcode.com/paper/a-deep-architecture-for-non-projective	A Deep Architecture for Non-Projective Dependency Parsing	null	https://aclanthology.org/W15-1508	https://aclanthology.org/W15-1508.pdf	https://github.com/erickrf/nlpnet	true	true	false	none
https://paperswithcode.com/paper/shapeconv-shape-aware-convolutional-layer-for	ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation	2108.10528	https://arxiv.org/abs/2108.10528v1	https://arxiv.org/pdf/2108.10528v1.pdf	https://github.com/hanchaoleng/shapeconv	true	true	true	pytorch
https://paperswithcode.com/paper/efficacy-of-bert-embeddings-on-predicting	Efficacy of BERT embeddings on predicting disaster from Twitter data	2108.10698	https://arxiv.org/abs/2108.10698v1	https://arxiv.org/pdf/2108.10698v1.pdf	https://github.com/ashischanda/sentiment-analysis	true	true	false	pytorch
https://paperswithcode.com/paper/deep-learning-of-human-visual-sensitivity-in	Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework	null	http://openaccess.thecvf.com/content_cvpr_2017/html/Kim_Deep_Learning_of_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Kim_Deep_Learning_of_CVPR_2017_paper.pdf	https://github.com/LeonLIU08/DeepQA-with-Pytorch	false	false	false	pytorch
https://paperswithcode.com/paper/cnn-based-autoencoder-application-in-breast	CNN Based Autoencoder Application in Breast Cancer Image Retrieval	null	https://ieeexplore.ieee.org/document/9502205	https://ieeexplore.ieee.org/document/9502205	https://github.com/forderation/breast-cancer-retrieval	true	false	false	tf
https://paperswithcode.com/paper/memorization-precedes-generation-learning	Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks	1803.01500	http://arxiv.org/abs/1803.01500v2	http://arxiv.org/pdf/1803.01500v2.pdf	https://github.com/whyjay/memoryGAN	true	true	false	tf
https://paperswithcode.com/paper/generic-approaches-for-parallel-rule-matching	Generic approaches for parallel rule matching in learning classifier systems	null	https://dl.acm.org/doi/10.1145/3377929.3398102	https://dl.acm.org/doi/10.1145/3377929.3398102	https://github.com/LagLukas/para_matching	false	false	false	none
https://paperswithcode.com/paper/an-efficient-lorentz-equivariant-graph-neural	An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging	2201.08187	https://arxiv.org/abs/2201.08187v6	https://arxiv.org/pdf/2201.08187v6.pdf	https://github.com/sdogsq/LorentzNet-release	true	true	false	pytorch
https://paperswithcode.com/paper/a-polytopal-method-for-the-brinkman-problem	A polytopal method for the Brinkman problem robust in all regimes	2301.03272	https://arxiv.org/abs/2301.03272v3	https://arxiv.org/pdf/2301.03272v3.pdf	https://github.com/jdroniou/HArDCore3D-release	true	false	false	none
https://paperswithcode.com/paper/accelerating-verified-compiler-development	Accelerating Verified-Compiler Development with a Verified Rewriting Engine	2205.00862	https://arxiv.org/abs/2205.00862v4	https://arxiv.org/pdf/2205.00862v4.pdf	https://github.com/mit-plv/rewriter	true	true	false	none
https://paperswithcode.com/paper/energetic-formulation-of-large-deformation	Energetic Formulation of Large-Deformation Poroelasticity	2112.15298	https://arxiv.org/abs/2112.15298v1	https://arxiv.org/pdf/2112.15298v1.pdf	https://github.com/minakari/poromechanics	true	true	false	none
https://paperswithcode.com/paper/transgan-two-transformers-can-make-one-strong	TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up	2102.07074	https://arxiv.org/abs/2102.07074v4	https://arxiv.org/pdf/2102.07074v4.pdf	https://github.com/omihub777/vit-cifar	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.