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/transformer-interpretability-beyond-attention	Transformer Interpretability Beyond Attention Visualization	2012.09838	https://arxiv.org/abs/2012.09838v2	https://arxiv.org/pdf/2012.09838v2.pdf	https://github.com/TiagoFilipeSousaGoncalves/survey-attention-medical-imaging	false	false	true	pytorch
https://paperswithcode.com/paper/optimal-vaccination-at-high-reproductive	Optimal vaccination at high reproductive numbers: sharp transitions and counter-intuitive allocations	2202.03909	https://arxiv.org/abs/2202.03909v1	https://arxiv.org/pdf/2202.03909v1.pdf	https://github.com/ngavish/highr0	true	true	false	none
https://paperswithcode.com/paper/spineparsenet-spine-parsing-for-volumetric-mr	SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation	null	https://ieeexplore.ieee.org/document/9201093	https://ieeexplore.ieee.org/document/9201093	https://github.com/pangshumao/SpineParseNet	false	false	false	pytorch
https://paperswithcode.com/paper/exploiting-anti-monotonicity-of-multi-label	Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules	1812.06833	http://arxiv.org/abs/1812.06833v1	http://arxiv.org/pdf/1812.06833v1.pdf	https://github.com/keelm/SeCo-MLC	true	true	true	none
https://paperswithcode.com/paper/efficient-discovery-of-expressive-multi-label	Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning	1908.06874	https://arxiv.org/abs/1908.06874v1	https://arxiv.org/pdf/1908.06874v1.pdf	https://github.com/keelm/SeCo-MLC	true	true	true	none
https://paperswithcode.com/paper/towards-demystifying-representation-learning-1	Towards Demystifying Representation Learning with Non-contrastive Self-supervision	2110.04947	https://arxiv.org/abs/2110.04947v2	https://arxiv.org/pdf/2110.04947v2.pdf	https://github.com/miszkur/SelfSupervisedLearning	false	false	true	tf
https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to	Bootstrap your own latent: A new approach to self-supervised Learning	2006.07733	https://arxiv.org/abs/2006.07733v3	https://arxiv.org/pdf/2006.07733v3.pdf	https://github.com/miszkur/SelfSupervisedLearning	false	false	true	tf
https://paperswithcode.com/paper/grad-cam-visual-explanations-from-deep	Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	1610.02391	https://arxiv.org/abs/1610.02391v4	https://arxiv.org/pdf/1610.02391v4.pdf	https://github.com/karandesaiii/CheXNet	false	false	true	pytorch
https://paperswithcode.com/paper/chexnet-radiologist-level-pneumonia-detection	CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning	1711.05225	http://arxiv.org/abs/1711.05225v3	http://arxiv.org/pdf/1711.05225v3.pdf	https://github.com/karandesaiii/CheXNet	false	false	true	pytorch
https://paperswithcode.com/paper/learning-to-diagnose-from-scratch-by	Learning to diagnose from scratch by exploiting dependencies among labels	1710.10501	http://arxiv.org/abs/1710.10501v2	http://arxiv.org/pdf/1710.10501v2.pdf	https://github.com/karandesaiii/CheXNet	false	false	true	pytorch
https://paperswithcode.com/paper/chestx-ray8-hospital-scale-chest-x-ray	ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases	1705.02315	http://arxiv.org/abs/1705.02315v5	http://arxiv.org/pdf/1705.02315v5.pdf	https://github.com/karandesaiii/CheXNet	false	false	true	pytorch
https://paperswithcode.com/paper/the-r2d2-prior-for-generalized-linear-mixed	The R2D2 Prior for Generalized Linear Mixed Models	2111.10718	https://arxiv.org/abs/2111.10718v3	https://arxiv.org/pdf/2111.10718v3.pdf	https://github.com/eyanchenko/r2d2glmm	true	true	true	none
https://paperswithcode.com/paper/how-to-fine-tune-bert-for-text-classification	How to Fine-Tune BERT for Text Classification?	1905.05583	https://arxiv.org/abs/1905.05583v3	https://arxiv.org/pdf/1905.05583v3.pdf	https://github.com/Derposoft/ai-educator	false	false	true	none
https://paperswithcode.com/paper/the-network-structure-of-cultural-distances	Cultures as networks of cultural traits: A unifying framework for measuring culture and cultural distances	2007.02359	https://arxiv.org/abs/2007.02359v5	https://arxiv.org/pdf/2007.02359v5.pdf	https://github.com/rrondinelli/cultural-networks	true	true	false	none
https://paperswithcode.com/paper/deformable-detr-deformable-transformers-for-1	Deformable DETR: Deformable Transformers for End-to-End Object Detection	2010.04159	https://arxiv.org/abs/2010.04159v4	https://arxiv.org/pdf/2010.04159v4.pdf	https://github.com/Li-ai-cell/Interpretation_DETR	false	false	true	pytorch
https://paperswithcode.com/paper/end-to-end-object-detection-with-transformers	End-to-End Object Detection with Transformers	2005.12872	https://arxiv.org/abs/2005.12872v3	https://arxiv.org/pdf/2005.12872v3.pdf	https://github.com/Li-ai-cell/Interpretation_DETR	false	false	true	pytorch
https://paperswithcode.com/paper/maslow-s-hammer-for-catastrophic-forgetting	Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation	2205.09029	https://arxiv.org/abs/2205.09029v1	https://arxiv.org/pdf/2205.09029v1.pdf	https://github.com/seblee97/student_teacher_catastrophic	true	true	false	pytorch
https://paperswithcode.com/paper/auto-encoding-score-distribution-regression	Auto-Encoding Score Distribution Regression for Action Quality Assessment	2111.11029	https://arxiv.org/abs/2111.11029v2	https://arxiv.org/pdf/2111.11029v2.pdf	https://github.com/InfoX-SEU/DAE-AQA	true	true	true	pytorch
https://paperswithcode.com/paper/efficient-quantized-sparse-matrix-operations	Efficient Quantized Sparse Matrix Operations on Tensor Cores	2209.06979	https://arxiv.org/abs/2209.06979v4	https://arxiv.org/pdf/2209.06979v4.pdf	https://github.com/shigangli/magicube	true	true	false	pytorch
https://paperswithcode.com/paper/towards-better-stability-and-adaptability	Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.pdf	https://github.com/dzhaoxd/dt-st	true	true	false	pytorch
https://paperswithcode.com/paper/random-quantum-neural-networks-rqnn-for-noisy	Random Quantum Neural Networks (RQNN) for Noisy Image Recognition	2203.01764	https://arxiv.org/abs/2203.01764v1	https://arxiv.org/pdf/2203.01764v1.pdf	https://github.com/darthsimpus/RQNN	true	true	false	pytorch
https://paperswithcode.com/paper/selecting-the-best-optimizing-system	Selecting the Best Optimizing System	2201.03065	https://arxiv.org/abs/2201.03065v1	https://arxiv.org/pdf/2201.03065v1.pdf	https://github.com/nian-si/selectoptsys	true	true	false	none
https://paperswithcode.com/paper/exploiting-adapters-for-cross-lingual-low	Exploiting Adapters for Cross-lingual Low-resource Speech Recognition	2105.11905	https://arxiv.org/abs/2105.11905v2	https://arxiv.org/pdf/2105.11905v2.pdf	https://github.com/jindongwang/transferlearning	true	true	true	pytorch
https://paperswithcode.com/paper/rate-coding-or-direct-coding-which-one-is	Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?	2202.03133	https://arxiv.org/abs/2202.03133v2	https://arxiv.org/pdf/2202.03133v2.pdf	https://github.com/intelligent-computing-lab-yale/rate-vs-direct	true	true	true	pytorch
https://paperswithcode.com/paper/frozen-in-time-a-joint-video-and-image	Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval	2104.00650	https://arxiv.org/abs/2104.00650v2	https://arxiv.org/pdf/2104.00650v2.pdf	https://github.com/princetonvisualai/mqvr	false	false	true	pytorch
https://paperswithcode.com/paper/token-labeling-training-a-85-5-top-1-accuracy	All Tokens Matter: Token Labeling for Training Better Vision Transformers	2104.10858	https://arxiv.org/abs/2104.10858v3	https://arxiv.org/pdf/2104.10858v3.pdf	https://github.com/sail-sg/dualformer	false	false	true	pytorch
https://paperswithcode.com/paper/grad-cam-visual-explanations-from-deep	Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	1610.02391	https://arxiv.org/abs/1610.02391v4	https://arxiv.org/pdf/1610.02391v4.pdf	https://github.com/sail-sg/dualformer	false	false	true	pytorch
https://paperswithcode.com/paper/multi-scale-one-class-recurrent-neural	Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection	2008.13361	https://arxiv.org/abs/2008.13361v1	https://arxiv.org/pdf/2008.13361v1.pdf	https://github.com/KnowledgeDiscovery/OC4Seq	true	false	false	pytorch
https://paperswithcode.com/paper/narwhal-and-tusk-a-dag-based-mempool-and	Narwhal and Tusk: A DAG-based Mempool and Efficient BFT Consensus	2105.11827	https://arxiv.org/abs/2105.11827v4	https://arxiv.org/pdf/2105.11827v4.pdf	https://github.com/asonnino/narwhal	true	true	true	none
https://paperswithcode.com/paper/multi-uav-path-planning-for-wireless-data	Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning	2010.12461	https://arxiv.org/abs/2010.12461v3	https://arxiv.org/pdf/2010.12461v3.pdf	https://github.com/hbayerlein/uav_data_harvesting	true	true	true	tf
https://paperswithcode.com/paper/multivariate-functional-group-sparse	Multivariate functional group sparse regression: functional predictor selection	2107.02146	https://arxiv.org/abs/2107.02146v2	https://arxiv.org/pdf/2107.02146v2.pdf	https://github.com/Ali-Mahzarnia/MFSGrp	true	true	true	none
https://paperswithcode.com/paper/graph-convolutional-modules-for-traffic	DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting	1905.12256	https://arxiv.org/abs/1905.12256v3	https://arxiv.org/pdf/1905.12256v3.pdf	https://github.com/snu-adsl/DDP-GCN	true	true	true	tf
https://paperswithcode.com/paper/inferring-dark-matter-substructure-with	Inferring dark matter substructure with astrometric lensing beyond the power spectrum	2110.01620	https://arxiv.org/abs/2110.01620v2	https://arxiv.org/pdf/2110.01620v2.pdf	https://github.com/smsharma/neural-global-astrometry	true	true	true	pytorch
https://paperswithcode.com/paper/3d-shape-reconstruction-from-free-hand	3D Shape Reconstruction from Free-Hand Sketches	2006.09694	https://arxiv.org/abs/2006.09694v2	https://arxiv.org/pdf/2006.09694v2.pdf	https://github.com/samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches	true	true	true	tf
https://paperswithcode.com/paper/learning-selection-masks-for-deep-neural	Input Selection for Bandwidth-Limited Neural Network Inference	1906.04673	https://arxiv.org/abs/1906.04673v2	https://arxiv.org/pdf/1906.04673v2.pdf	https://github.com/stefoe/selection-masks	true	true	true	pytorch
https://paperswithcode.com/paper/transmil-transformer-based-correlated	TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification	2106.00908	https://arxiv.org/abs/2106.00908v2	https://arxiv.org/pdf/2106.00908v2.pdf	https://github.com/Ycblue/TransMIL	false	false	true	pytorch
https://paperswithcode.com/paper/boosting-multiple-sclerosis-lesion	Boosting multiple sclerosis lesion segmentation through attention mechanism	2304.10790	https://arxiv.org/abs/2304.10790v1	https://arxiv.org/pdf/2304.10790v1.pdf	https://github.com/ictlab-unict/attention-cnn-MS-segmentation	true	false	false	pytorch
https://paperswithcode.com/paper/latent-autoregressive-source-separation	Latent Autoregressive Source Separation	2301.08562	https://arxiv.org/abs/2301.08562v1	https://arxiv.org/pdf/2301.08562v1.pdf	https://github.com/gladia-research-group/latent-autoregressive-source-separation	true	false	false	pytorch
https://paperswithcode.com/paper/on-approximate-data-reduction-for-the-rural	On approximate data reduction for the Rural Postman Problem: Theory and experiments	1812.10131	http://arxiv.org/abs/1812.10131v3	http://arxiv.org/pdf/1812.10131v3.pdf	https://gitlab.com/rvb/rpp-psaks	true	true	true	none
https://paperswithcode.com/paper/hvs-inspired-signal-degradation-network-for	HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation	2208.07583	https://arxiv.org/abs/2208.07583v1	https://arxiv.org/pdf/2208.07583v1.pdf	https://github.com/jianjin008/hvs-sd-jnd	true	true	false	none
https://paperswithcode.com/paper/improving-unsupervised-defect-segmentation-by	Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders	1807.02011	http://arxiv.org/abs/1807.02011v3	http://arxiv.org/pdf/1807.02011v3.pdf	https://github.com/meitalB/NN	false	false	true	tf
https://paperswithcode.com/paper/semantic-communication-an-information	Semantic Information Recovery in Wireless Networks	2204.13366	https://arxiv.org/abs/2204.13366v4	https://arxiv.org/pdf/2204.13366v4.pdf	https://github.com/ant-uni-bremen/sinfony	true	false	false	tf
https://paperswithcode.com/paper/qagan-adversarial-approach-to-learning-domain	QAGAN: Adversarial Approach To Learning Domain Invariant Language Features	2206.12388	https://arxiv.org/abs/2206.12388v1	https://arxiv.org/pdf/2206.12388v1.pdf	https://github.com/towardsautonomy/qagan	true	true	false	pytorch
https://paperswithcode.com/paper/adarnn-adaptive-learning-and-forecasting-of	AdaRNN: Adaptive Learning and Forecasting of Time Series	2108.04443	https://arxiv.org/abs/2108.04443v2	https://arxiv.org/pdf/2108.04443v2.pdf	https://github.com/jindongwang/transferlearning	true	true	true	pytorch
https://paperswithcode.com/paper/kan-kolmogorov-arnold-networks	KAN: Kolmogorov-Arnold Networks	2404.19756	https://arxiv.org/abs/2404.19756v5	https://arxiv.org/pdf/2404.19756v5.pdf	https://github.com/Ipsedo/KolmogorovArnoldNetworks	false	false	false	pytorch
https://paperswithcode.com/paper/searching-for-activation-functions	Searching for Activation Functions	1710.05941	http://arxiv.org/abs/1710.05941v2	http://arxiv.org/pdf/1710.05941v2.pdf	https://github.com/ShubAn1901/License-Plate-Recognition	false	false	true	pytorch
https://paperswithcode.com/paper/temporal-robustness-of-stochastic-signals	Temporal Robustness of Stochastic Signals	2202.02583	https://arxiv.org/abs/2202.02583v2	https://arxiv.org/pdf/2202.02583v2.pdf	https://github.com/temporalrobrisk/temporal-robustness-risk	true	true	false	none
https://paperswithcode.com/paper/hypergraph-neural-networks-for-hypergraph	Hypergraph Neural Networks for Hypergraph Matching	null	http://openaccess.thecvf.com//content/ICCV2021/html/Liao_Hypergraph_Neural_Networks_for_Hypergraph_Matching_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Liao_Hypergraph_Neural_Networks_for_Hypergraph_Matching_ICCV_2021_paper.pdf	https://github.com/xwliao/hnn-hm	true	true	false	none
https://paperswithcode.com/paper/libact-pool-based-active-learning-in-python	libact: Pool-based Active Learning in Python	1710.00379	http://arxiv.org/abs/1710.00379v1	http://arxiv.org/pdf/1710.00379v1.pdf	https://github.com/melkherj/puddle	false	false	true	none
https://paperswithcode.com/paper/realistic-evaluation-of-deep-semi-supervised	Realistic Evaluation of Deep Semi-Supervised Learning Algorithms	1804.09170	https://arxiv.org/abs/1804.09170v4	https://arxiv.org/pdf/1804.09170v4.pdf	https://github.com/melkherj/puddle	false	false	true	none
https://paperswithcode.com/paper/deep-predictive-coding-networks-for-video	Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning	1605.08104	http://arxiv.org/abs/1605.08104v5	http://arxiv.org/pdf/1605.08104v5.pdf	https://github.com/nikolasmcneal/music-prediction	false	false	true	pytorch
https://paperswithcode.com/paper/curriculum-learning-with-infant-egocentric	Curriculum Learning With Infant Egocentric Videos	null	https://openreview.net/forum?id=zkfyOkBVpz	https://openreview.net/pdf?id=zkfyOkBVpz	https://github.com/ssheybani/baby-vision-curriculum	true	false	false	pytorch
https://paperswithcode.com/paper/channel-aware-contrastive-conditional	Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting	2410.02168	https://arxiv.org/abs/2410.02168v1	https://arxiv.org/pdf/2410.02168v1.pdf	https://github.com/LSY-Cython/CCDM	true	true	false	pytorch
https://paperswithcode.com/paper/a-dual-input-aware-factorization-machine-for	A Dual Input-aware Factorization Machine for CTR Prediction	null	https://www.ijcai.org/proceedings/2020/434	https://www.ijcai.org/proceedings/2020/0434.pdf	https://github.com/Andy1314Chen/DIFM-Paddle	false	false	false	paddle
https://paperswithcode.com/paper/xci-sketch-extraction-of-color-information	XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches	2108.11554	https://arxiv.org/abs/2108.11554v2	https://arxiv.org/pdf/2108.11554v2.pdf	https://github.com/Sampai28/XCI-Sketch	false	false	true	none
https://paperswithcode.com/paper/constrained-policy-optimization	Constrained Policy Optimization	1705.10528	http://arxiv.org/abs/1705.10528v1	http://arxiv.org/pdf/1705.10528v1.pdf	https://github.com/Bigpig4396/PyTorch-Constrained-Policy-Optimization-CPO	false	false	true	pytorch
https://paperswithcode.com/paper/autolabel-clip-based-framework-for-open-set	AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation	2304.01110	https://arxiv.org/abs/2304.01110v2	https://arxiv.org/pdf/2304.01110v2.pdf	https://github.com/gzaraunitn/autolabel	true	true	true	pytorch
https://paperswithcode.com/paper/respecting-time-series-properties-makes-deep	Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect	2207.10941	https://arxiv.org/abs/2207.10941v1	https://arxiv.org/pdf/2207.10941v1.pdf	https://github.com/origamisl/rtnet	true	true	true	pytorch
https://paperswithcode.com/paper/algorithms-and-radiation-dynamics-for-the	Algorithms and radiation dynamics for the vicinity of black holes I. Methods and codes	2212.01532	https://arxiv.org/abs/2212.01532v1	https://arxiv.org/pdf/2212.01532v1.pdf	https://gitlab.com/leelamichaels/Tranquillity	false	true	false	none
https://paperswithcode.com/paper/leaping-through-tree-space-continuous	Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees	2306.05739	https://arxiv.org/abs/2306.05739v4	https://arxiv.org/pdf/2306.05739v4.pdf	https://github.com/neclow/gradme	true	true	false	jax
https://paperswithcode.com/paper/biomimetic-tactile-receptors-for-3d-printed	Artificial SA-I and RA-I Afferents for Tactile Sensing of Ridges and Gratings	2107.02084	https://arxiv.org/abs/2107.02084v3	https://arxiv.org/pdf/2107.02084v3.pdf	https://github.com/nlepora/afferents-tactile-gratings-jrsi2022	true	true	false	none
https://paperswithcode.com/paper/exploring-the-state-of-the-art-language	Transformers on Multilingual Clause-Level Morphology	2211.01736	https://arxiv.org/abs/2211.01736v2	https://arxiv.org/pdf/2211.01736v2.pdf	https://github.com/emrecanacikgoz/mrl2022	true	true	false	pytorch
https://paperswithcode.com/paper/lovecon-text-driven-training-free-long-video	LOVECon: Text-driven Training-Free Long Video Editing with ControlNet	2310.09711	https://arxiv.org/abs/2310.09711v3	https://arxiv.org/pdf/2310.09711v3.pdf	https://github.com/zhijie-group/lovecon	true	true	false	pytorch
https://paperswithcode.com/paper/simulations-of-crystal-nucleation-from	Simulations of Crystal Nucleation from Solution at Constant Chemical Potential	1907.04037	https://arxiv.org/abs/1907.04037v1	https://arxiv.org/pdf/1907.04037v1.pdf	https://github.com/Tarakk/plumed-cumd	false	false	true	none
https://paperswithcode.com/paper/convex-clustering-through-mm-an-efficient	Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering	2211.01877	https://arxiv.org/abs/2211.01877v2	https://arxiv.org/pdf/2211.01877v2.pdf	https://github.com/djwtouw/ccmm-paper	true	true	false	none
https://paperswithcode.com/paper/argscichat-a-dataset-for-argumentative	ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers	2202.06690	https://arxiv.org/abs/2202.06690v3	https://arxiv.org/pdf/2202.06690v3.pdf	https://github.com/federicoruggeri/argscichat_project	true	true	true	none
https://paperswithcode.com/paper/hawkeye-a-pytorch-based-library-for-fine	Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning	2310.09600	https://arxiv.org/abs/2310.09600v2	https://arxiv.org/pdf/2310.09600v2.pdf	https://github.com/hawkeye-finegrained/hawkeye	true	true	false	pytorch
https://paperswithcode.com/paper/twitter-dataset-for-2022-russo-ukrainian	Twitter Dataset for 2022 Russo-Ukrainian Crisis	2203.02955	https://arxiv.org/abs/2203.02955v1	https://arxiv.org/pdf/2203.02955v1.pdf	https://github.com/ehsanulhaq1/russo_ukraine_dataset	true	true	true	none
https://paperswithcode.com/paper/an-approach-for-generating-families-of	An Approach for Generating Families of Energetically Optimal Gaits from Passive Dynamic Walking Gaits	2303.14750	https://arxiv.org/abs/2303.14750v2	https://arxiv.org/pdf/2303.14750v2.pdf	https://github.com/nr-codes/optimalgaitsforcompassgait	true	true	false	none
https://paperswithcode.com/paper/how-much-does-attention-actually-attend	How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers	2211.03495	https://arxiv.org/abs/2211.03495v1	https://arxiv.org/pdf/2211.03495v1.pdf	https://github.com/schwartz-lab-nlp/papa	true	true	true	pytorch
https://paperswithcode.com/paper/on-first-order-meta-learning-algorithms	On First-Order Meta-Learning Algorithms	1803.02999	http://arxiv.org/abs/1803.02999v3	http://arxiv.org/pdf/1803.02999v3.pdf	https://github.com/Yuzhe-CHEN/NerfSNN	false	false	true	pytorch
https://paperswithcode.com/paper/semantics-adaptive-activation-intervention	Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors	2410.12299	https://arxiv.org/abs/2410.12299v1	https://arxiv.org/pdf/2410.12299v1.pdf	https://github.com/weixuan-wang123/SADI	true	true	true	pytorch
https://paperswithcode.com/paper/a-recommender-system-for-automatic-picking-of	A recommender system for automatic picking of subsurface formation tops	2202.08869	https://arxiv.org/abs/2202.08869v1	https://arxiv.org/pdf/2202.08869v1.pdf	https://github.com/jessepisel/matrixfactorization	true	true	false	none
https://paperswithcode.com/paper/disco-remedy-self-supervised-learning-on	DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning	2104.09124	https://arxiv.org/abs/2104.09124v7	https://arxiv.org/pdf/2104.09124v7.pdf	https://github.com/Yuting-Gao/DisCo-pytorch	true	true	true	pytorch
https://paperswithcode.com/paper/dual-path-cnn-with-max-gated-block-for-text	Dual-path CNN with Max Gated block for Text-Based Person Re-identification	2009.09343	https://arxiv.org/abs/2009.09343v1	https://arxiv.org/pdf/2009.09343v1.pdf	https://github.com/voriarty/Dual-path-CNN-with-Max-Gated-block-for-Text-Based-Person-Re-identification	true	false	false	pytorch
https://paperswithcode.com/paper/solving-satisfiability-modulo-counting-for	Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees	2309.08883	https://arxiv.org/abs/2309.08883v2	https://arxiv.org/pdf/2309.08883v2.pdf	https://github.com/jil016/xor-smc	true	true	false	none
https://paperswithcode.com/paper/on-automatic-feasibility-study-for-machine	Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise	2010.08410	https://arxiv.org/abs/2010.08410v4	https://arxiv.org/pdf/2010.08410v4.pdf	https://github.com/ds3lab/snoopy-paper	true	true	false	tf
https://paperswithcode.com/paper/residual-network-and-embedding-usage-new	Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks	2105.08330	https://arxiv.org/abs/2105.08330v2	https://arxiv.org/pdf/2105.08330v2.pdf	https://github.com/ytchx1999/GCN_res-CS-v2	false	false	true	pytorch
https://paperswithcode.com/paper/talking-heads-attention	Talking-Heads Attention	2003.02436	https://arxiv.org/abs/2003.02436v1	https://arxiv.org/pdf/2003.02436v1.pdf	https://github.com/zygmuntz/hyperband	true	false	false	none
https://paperswithcode.com/paper/interpretable-self-aware-neural-networks-for	Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction	2211.08701	https://arxiv.org/abs/2211.08701v1	https://arxiv.org/pdf/2211.08701v1.pdf	https://github.com/sisl/interpretableselfawareprediction	true	true	true	pytorch
https://paperswithcode.com/paper/bsn-boundary-sensitive-network-for-temporal	BSN: Boundary Sensitive Network for Temporal Action Proposal Generation	1806.02964	http://arxiv.org/abs/1806.02964v3	http://arxiv.org/pdf/1806.02964v3.pdf	https://github.com/wzmsltw/BSN-boundary-sensitive-network	false	true	true	pytorch
https://paperswithcode.com/paper/type-driven-multi-turn-corrections-for	Type-Driven Multi-Turn Corrections for Grammatical Error Correction	2203.09136	https://arxiv.org/abs/2203.09136v1	https://arxiv.org/pdf/2203.09136v1.pdf	https://github.com/deeplearnxmu/tmtc	true	true	false	pytorch
https://paperswithcode.com/paper/koopman-based-differentiable-predictive	Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem	2203.08984	https://arxiv.org/abs/2203.08984v1	https://arxiv.org/pdf/2203.08984v1.pdf	https://github.com/pnnl/neuromancer	true	true	false	pytorch
https://paperswithcode.com/paper/modeling-microlensing-events-with-mulensmodel	Modeling microlensing events with MulensModel	1803.01003	http://arxiv.org/abs/1803.01003v3	http://arxiv.org/pdf/1803.01003v3.pdf	https://github.com/rpoleski/MulensModel	true	true	true	none
https://paperswithcode.com/paper/edge-based-local-push-for-personalized	Edge-based Local Push for Personalized PageRank	2203.07937	https://arxiv.org/abs/2203.07937v2	https://arxiv.org/pdf/2203.07937v2.pdf	https://github.com/wanghzccls/edgepush	true	true	false	none
https://paperswithcode.com/paper/streaming-velocity-effects-on-the-post	Streaming Velocity Effects on the Post-reionization 21 cm Baryon Acoustic Oscillation Signal	2107.07615	https://arxiv.org/abs/2107.07615v2	https://arxiv.org/pdf/2107.07615v2.pdf	https://github.com/cosmosheep/hipowerspectrum	true	true	true	none
https://paperswithcode.com/paper/a-reduction-to-binary-approach-for-debiasing	A Reduction to Binary Approach for Debiasing Multiclass Datasets	2205.15860	https://arxiv.org/abs/2205.15860v2	https://arxiv.org/pdf/2205.15860v2.pdf	https://github.com/google-research/google-research/tree/master/ml_debiaser	true	false	false	jax
https://paperswithcode.com/paper/dverge-diversifying-vulnerabilities-for	DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles	2009.14720	https://arxiv.org/abs/2009.14720v2	https://arxiv.org/pdf/2009.14720v2.pdf	https://github.com/wang-axis/dna	false	false	true	pytorch
https://paperswithcode.com/paper/disrupting-adversarial-transferability-in	Disrupting Adversarial Transferability in Deep Neural Networks	2108.12492	https://arxiv.org/abs/2108.12492v3	https://arxiv.org/pdf/2108.12492v3.pdf	https://github.com/wang-axis/dna	true	true	false	pytorch
https://paperswithcode.com/paper/a-unified-transformer-framework-for-group	A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection	2203.04708	https://arxiv.org/abs/2203.04708v2	https://arxiv.org/pdf/2203.04708v2.pdf	https://github.com/suyukun666/UFO	true	true	true	pytorch
https://paperswithcode.com/paper/end-to-end-symbolic-regression-with	End-to-end symbolic regression with transformers	2204.10532	https://arxiv.org/abs/2204.10532v1	https://arxiv.org/pdf/2204.10532v1.pdf	https://github.com/facebookresearch/symbolicregression	true	true	true	pytorch
https://paperswithcode.com/paper/a-microlensing-search-of-700-million-vvv	A microlensing search of 700 million VVV light curves	2106.15617	https://arxiv.org/abs/2106.15617v1	https://arxiv.org/pdf/2106.15617v1.pdf	https://github.com/zofiakaczmarek/nested_ulens_parallax	false	false	true	none
https://paperswithcode.com/paper/dynesty-a-dynamic-nested-sampling-package-for	dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences	1904.02180	http://arxiv.org/abs/1904.02180v1	http://arxiv.org/pdf/1904.02180v1.pdf	https://github.com/zofiakaczmarek/nested_ulens_parallax	false	false	true	none
https://paperswithcode.com/paper/convert-an-application-to-faq-answering	ConveRT for FAQ Answering	2108.00719	https://arxiv.org/abs/2108.00719v3	https://arxiv.org/pdf/2108.00719v3.pdf	https://github.com/clips/ADATaLKS	false	false	true	none
https://paperswithcode.com/paper/an-accurate-unsupervised-method-for-joint	An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection	2203.05147	https://arxiv.org/abs/2203.05147v1	https://arxiv.org/pdf/2203.05147v1.pdf	https://github.com/luosx18/ued	true	true	false	pytorch
https://paperswithcode.com/paper/modeling-relational-data-with-graph	Modeling Relational Data with Graph Convolutional Networks	1703.06103	http://arxiv.org/abs/1703.06103v4	http://arxiv.org/pdf/1703.06103v4.pdf	https://github.com/shijx12/kqapro_baselines	false	false	true	pytorch
https://paperswithcode.com/paper/bart-denoising-sequence-to-sequence-pre	BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension	1910.13461	https://arxiv.org/abs/1910.13461v1	https://arxiv.org/pdf/1910.13461v1.pdf	https://github.com/shijx12/kqapro_baselines	false	false	true	pytorch
https://paperswithcode.com/paper/cross-modal-map-learning-for-vision-and	Cross-modal Map Learning for Vision and Language Navigation	2203.05137	https://arxiv.org/abs/2203.05137v3	https://arxiv.org/pdf/2203.05137v3.pdf	https://github.com/ggeorgak11/cm2	true	true	false	pytorch
https://paperswithcode.com/paper/large-scale-gan-training-for-high-fidelity	Large Scale GAN Training for High Fidelity Natural Image Synthesis	1809.11096	http://arxiv.org/abs/1809.11096v2	http://arxiv.org/pdf/1809.11096v2.pdf	https://github.com/abhi8585/GeneratedART-NFT-VISION	false	false	true	tf
https://paperswithcode.com/paper/self-attention-generative-adversarial	Self-Attention Generative Adversarial Networks	1805.08318	https://arxiv.org/abs/1805.08318v2	https://arxiv.org/pdf/1805.08318v2.pdf	https://github.com/abhi8585/GeneratedART-NFT-VISION	false	false	true	tf

Subsets and Splits

Framework Repo Connectivity Analysis

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

Deduplicated Paper-Code Links

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

Paper Repo Counts & Distribution

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

Quantum Papers with Code Links

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

Financial Stock Price Prediction

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

SQL Console for pwc-archive/links-between-paper-and-code

Retrieves specific details about a single paper by its arXiv ID, providing limited insight into the dataset.

Search for YOLO Links

Retrieves a limited set of records related to YOLO, providing basic information about papers and repositories but without deeper analysis.

Prompt Optimization and Personalization

Retrieves a limited set of papers with titles containing specific keywords related to prompt optimization and personalization, providing basic filtering of the dataset.