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/where-to-look-at-the-movies-analyzing-visual	Where to look at the movies : Analyzing visual attention to understand movie editing	2102.13378	https://arxiv.org/abs/2102.13378v1	https://arxiv.org/pdf/2102.13378v1.pdf	https://github.com/abruckert/eye_tracking_filmmaking	true	true	false	none
https://paperswithcode.com/paper/superaccurate-camera-calibration-via-inverse	Superaccurate Camera Calibration via Inverse Rendering	2003.09177	https://arxiv.org/abs/2003.09177v1	https://arxiv.org/pdf/2003.09177v1.pdf	https://github.com/MortenHannemose/pytorch-vfi-cft	true	false	false	pytorch
https://paperswithcode.com/paper/soccernet-a-scalable-dataset-for-action	SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos	1804.04527	http://arxiv.org/abs/1804.04527v2	http://arxiv.org/pdf/1804.04527v2.pdf	https://github.com/SilvioGiancola/SoccerNet-code	false	false	false	tf
https://paperswithcode.com/paper/fast-and-robust-multiple-colorchecker	Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks	1810.08639	http://arxiv.org/abs/1810.08639v1	http://arxiv.org/pdf/1810.08639v1.pdf	https://github.com/pedrodiamel/colorchacker-detection	true	true	true	none
https://paperswithcode.com/paper/skip-thought-vectors	Skip-Thought Vectors	1506.06726	http://arxiv.org/abs/1506.06726v1	http://arxiv.org/pdf/1506.06726v1.pdf	https://github.com/facebookresearch/InferSent	false	false	true	pytorch
https://paperswithcode.com/paper/vse-improving-visual-semantic-embeddings-with	VSE++: Improving Visual-Semantic Embeddings with Hard Negatives	1707.05612	http://arxiv.org/abs/1707.05612v4	http://arxiv.org/pdf/1707.05612v4.pdf	https://github.com/armandvilalta/Full-network-multimodal-embeddings	false	false	true	none
https://paperswithcode.com/paper/combining-monte-carlo-tree-search-and	Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring	2304.12146	https://arxiv.org/abs/2304.12146v1	https://arxiv.org/pdf/2304.12146v1.pdf	https://github.com/cyril-grelier/gc_wvcp_mcts	true	true	false	none
https://paperswithcode.com/paper/decision-stream-cultivating-deep-decision	Decision Stream: Cultivating Deep Decision Trees	1704.07657	http://arxiv.org/abs/1704.07657v3	http://arxiv.org/pdf/1704.07657v3.pdf	https://github.com/aiff22/Decision-Stream	true	true	true	none
https://paperswithcode.com/paper/leverage-eye-movement-data-for-saliency	How is Gaze Influenced by Image Transformations? Dataset and Model	1905.06803	https://arxiv.org/abs/1905.06803v4	https://arxiv.org/pdf/1905.06803v4.pdf	https://github.com/CZHQuality/Sal-CFS-GAN	true	true	false	tf
https://paperswithcode.com/paper/addressee-and-response-selection-in-multi	Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs	1709.04005	http://arxiv.org/abs/1709.04005v2	http://arxiv.org/pdf/1709.04005v2.pdf	https://github.com/ryanzhumich/sirnn	true	true	true	none
https://paperswithcode.com/paper/finite-sample-learning-of-moving-targets	Finite sample learning of moving targets	2408.04406	https://arxiv.org/abs/2408.04406v2	https://arxiv.org/pdf/2408.04406v2.pdf	https://github.com/nikovert/finite-sample-learning-of-moving-targets	false	false	false	none
https://paperswithcode.com/paper/interpret-federated-learning-with-shapley	Interpret Federated Learning with Shapley Values	1905.04519	https://arxiv.org/abs/1905.04519v1	https://arxiv.org/pdf/1905.04519v1.pdf	https://github.com/crownpku/federated_shap	true	true	true	none
https://paperswithcode.com/paper/lenient-multi-agent-deep-reinforcement	Lenient Multi-Agent Deep Reinforcement Learning	1707.04402	http://arxiv.org/abs/1707.04402v2	http://arxiv.org/pdf/1707.04402v2.pdf	https://github.com/gjp1203/nui_in_madrl	false	false	true	none
https://paperswithcode.com/paper/hierarchical-cross-modal-talking-face	Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss	1905.03820	https://arxiv.org/abs/1905.03820v1	https://arxiv.org/pdf/1905.03820v1.pdf	https://github.com/lelechen63/ATVGnet	true	true	false	pytorch
https://paperswithcode.com/paper/benchmarking-natural-language-understanding	Benchmarking Natural Language Understanding Services for building Conversational Agents	1903.05566	http://arxiv.org/abs/1903.05566v3	http://arxiv.org/pdf/1903.05566v3.pdf	https://github.com/lackel/hierarchical_weighted_scl	false	false	true	pytorch
https://paperswithcode.com/paper/hdltex-hierarchical-deep-learning-for-text	HDLTex: Hierarchical Deep Learning for Text Classification	1709.08267	http://arxiv.org/abs/1709.08267v2	http://arxiv.org/pdf/1709.08267v2.pdf	https://github.com/lackel/hierarchical_weighted_scl	false	false	true	pytorch
https://paperswithcode.com/paper/syntaxsqlnet-syntax-tree-networks-for-complex	SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task	1810.05237	http://arxiv.org/abs/1810.05237v2	http://arxiv.org/pdf/1810.05237v2.pdf	https://github.com/heyanger/sqltools	false	false	true	none
https://paperswithcode.com/paper/missing-data-infill-with-automunge-1	Missing Data Infill with Automunge	2202.09484	https://arxiv.org/abs/2202.09484v1	https://arxiv.org/pdf/2202.09484v1.pdf	https://github.com/gatorwatt/Paper_Demonstrations/tree/main/Missing_Data_infill	true	false	false	none
https://paperswithcode.com/paper/temporal-attentive-alignment-for-video-domain	Temporal Attentive Alignment for Video Domain Adaptation	1905.10861	https://arxiv.org/abs/1905.10861v5	https://arxiv.org/pdf/1905.10861v5.pdf	https://github.com/olivesgatech/TA3N	false	false	true	pytorch
https://paperswithcode.com/paper/hybrid-reward-architecture-for-reinforcement	Hybrid Reward Architecture for Reinforcement Learning	1706.04208	http://arxiv.org/abs/1706.04208v2	http://arxiv.org/pdf/1706.04208v2.pdf	https://github.com/KhenNguyn/DoAn3-MachineLearning	false	false	true	tf
https://paperswithcode.com/paper/robustness-may-be-at-odds-with-accuracy	Robustness May Be at Odds with Accuracy	1805.12152	https://arxiv.org/abs/1805.12152v5	https://arxiv.org/pdf/1805.12152v5.pdf	https://github.com/louis2889184/pytorch-adversarial-training	false	false	true	pytorch
https://paperswithcode.com/paper/hierarchy-of-visual-words-a-learning-based	Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval	1908.02786	https://arxiv.org/abs/1908.02786v1	https://arxiv.org/pdf/1908.02786v1.pdf	https://github.com/Prograf-UFF/HoVW	true	true	true	none
https://paperswithcode.com/paper/deep-reinforcement-learning-from-human	Deep reinforcement learning from human preferences	1706.03741	https://arxiv.org/abs/1706.03741v4	https://arxiv.org/pdf/1706.03741v4.pdf	https://github.com/vcharvet/project-rl	false	false	true	tf
https://paperswithcode.com/paper/neural-motifs-scene-graph-parsing-with-global	Neural Motifs: Scene Graph Parsing with Global Context	1711.06640	http://arxiv.org/abs/1711.06640v2	http://arxiv.org/pdf/1711.06640v2.pdf	https://github.com/HCPLab-SYSU/KERN	false	false	true	pytorch
https://paperswithcode.com/paper/scene-relighting-with-illumination-estimation	Scene relighting with illumination estimation in the latent space on an encoder-decoder scheme	2006.02333	https://arxiv.org/abs/2006.02333v1	https://arxiv.org/pdf/2006.02333v1.pdf	https://github.com/martin-ev/2DSceneRelighting	true	true	true	pytorch
https://paperswithcode.com/paper/m-fuse-multi-frame-fusion-for-scene-flow	M-FUSE: Multi-frame Fusion for Scene Flow Estimation	2207.05704	https://arxiv.org/abs/2207.05704v2	https://arxiv.org/pdf/2207.05704v2.pdf	https://github.com/cv-stuttgart/m-fuse	true	true	true	pytorch
https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic-1	Fully Convolutional Networks for Semantic Segmentation	1411.4038	http://arxiv.org/abs/1411.4038v2	http://arxiv.org/pdf/1411.4038v2.pdf	https://github.com/giovanniguidi/FCN-keras	false	false	true	none
https://paperswithcode.com/paper/spurious-local-minima-are-common-in-two-layer	Spurious Local Minima are Common in Two-Layer ReLU Neural Networks	1712.08968	http://arxiv.org/abs/1712.08968v3	http://arxiv.org/pdf/1712.08968v3.pdf	https://github.com/ItaySafran/OneLayerGDconvergence	true	true	false	none
https://paperswithcode.com/paper/co-trained-convolutional-neural-networks-for	Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI	null	https://www.ncbi.nlm.nih.gov/pubmed/28850876	https://www.ncbi.nlm.nih.gov/pubmed/28850876	https://github.com/Andysis/co-trained-CADx	false	false	false	none
https://paperswithcode.com/paper/improving-retinanet-for-ct-lesion-detection	Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels	1906.02283	https://arxiv.org/abs/1906.02283v1	https://arxiv.org/pdf/1906.02283v1.pdf	https://github.com/fizyr/keras-retinanet	false	false	true	tf
https://paperswithcode.com/paper/one-shot-video-object-segmentation	One-Shot Video Object Segmentation	1611.05198	http://arxiv.org/abs/1611.05198v4	http://arxiv.org/pdf/1611.05198v4.pdf	https://github.com/kmaninis/OSVOS-caffe	false	false	false	tf
https://paperswithcode.com/paper/non-turing-computations-via-malament-hogarth	Non-Turing computations via Malament-Hogarth space-times	gr-qc/0104023	https://arxiv.org/abs/gr-qc/0104023v2	https://arxiv.org/pdf/gr-qc/0104023v2.pdf	https://github.com/alexnieddu/Kerr-Black-Hole-Geodesics	false	false	true	none
https://paperswithcode.com/paper/a-chi-squared-time-frequency-discriminator	A chi-squared time-frequency discriminator for gravitational wave detection	gr-qc/0405045	https://arxiv.org/abs/gr-qc/0405045v2	https://arxiv.org/pdf/gr-qc/0405045v2.pdf	https://github.com/gwastro/1-ogc	false	false	true	none
https://paperswithcode.com/paper/quantum-associative-memory	Quantum Associative Memory	quant-ph/9807053	https://arxiv.org/abs/quant-ph/9807053v1	https://arxiv.org/pdf/quant-ph/9807053v1.pdf	https://github.com/hhy37/Liquid	false	false	true	none
https://paperswithcode.com/paper/improved-simulation-of-stabilizer-circuits	Improved Simulation of Stabilizer Circuits	quant-ph/0406196	https://arxiv.org/abs/quant-ph/0406196v5	https://arxiv.org/pdf/quant-ph/0406196v5.pdf	https://github.com/hhy37/Liquid	false	false	true	none
https://paperswithcode.com/paper/distributed-prioritized-experience-replay	Distributed Prioritized Experience Replay	1803.00933	http://arxiv.org/abs/1803.00933v1	http://arxiv.org/pdf/1803.00933v1.pdf	https://github.com/neka-nat/distributed_rl	false	false	true	pytorch
https://paperswithcode.com/paper/benchmarking-automatic-machine-learning	Benchmarking Automatic Machine Learning Frameworks	1808.06492	http://arxiv.org/abs/1808.06492v1	http://arxiv.org/pdf/1808.06492v1.pdf	https://github.com/ClimbsRocks/auto_ml	false	true	false	tf
https://paperswithcode.com/paper/a-fofe-based-local-detection-approach-for	A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection	1611.00801	http://arxiv.org/abs/1611.00801v1	http://arxiv.org/pdf/1611.00801v1.pdf	https://github.com/xmb-cipher/fofe-ner	true	true	true	tf
https://paperswithcode.com/paper/nonnegative-decomposition-of-multivariate	Nonnegative Decomposition of Multivariate Information	1004.2515	http://arxiv.org/abs/1004.2515v1	http://arxiv.org/pdf/1004.2515v1.pdf	https://github.com/robince/partial-info-decomp	false	false	true	none
https://paperswithcode.com/paper/a-tutorial-on-thompson-sampling	A Tutorial on Thompson Sampling	1707.02038	https://arxiv.org/abs/1707.02038v3	https://arxiv.org/pdf/1707.02038v3.pdf	https://github.com/iosband/ts_tutorial	true	true	false	none
https://paperswithcode.com/paper/penalizing-unfairness-in-binary	Penalizing Unfairness in Binary Classification	1707.00044	http://arxiv.org/abs/1707.00044v3	http://arxiv.org/pdf/1707.00044v3.pdf	https://github.com/jjgold012/lab-project-fairness	true	true	false	none
https://paperswithcode.com/paper/teacher-student-curriculum-learning	Teacher-Student Curriculum Learning	1707.00183	http://arxiv.org/abs/1707.00183v2	http://arxiv.org/pdf/1707.00183v2.pdf	https://github.com/tambetm/TSCL	true	true	true	tf
https://paperswithcode.com/paper/text-matching-as-image-recognition	Text Matching as Image Recognition	1602.06359	http://arxiv.org/abs/1602.06359v1	http://arxiv.org/pdf/1602.06359v1.pdf	https://github.com/pl8787/MatchPyramid-TensorFlow	false	false	true	tf
https://paperswithcode.com/paper/hyperband-a-novel-bandit-based-approach-to	Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization	1603.06560	http://arxiv.org/abs/1603.06560v4	http://arxiv.org/pdf/1603.06560v4.pdf	https://github.com/zygmuntz/hyperband	false	false	false	none
https://paperswithcode.com/paper/perceptual-losses-for-real-time-style	Perceptual Losses for Real-Time Style Transfer and Super-Resolution	1603.08155	http://arxiv.org/abs/1603.08155v1	http://arxiv.org/pdf/1603.08155v1.pdf	https://github.com/ksivaman/super-res	false	false	true	pytorch
https://paperswithcode.com/paper/optimal-transport-based-machine-learning-to	Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data	2107.11192	https://arxiv.org/abs/2107.11192v3	https://arxiv.org/pdf/2107.11192v3.pdf	https://github.com/yen-nguyen-thi-thanh/wtot_coclust_match	true	true	false	pytorch
https://paperswithcode.com/paper/mushroomrl-simplifying-reinforcement-learning	MushroomRL: Simplifying Reinforcement Learning Research	2001.01102	https://arxiv.org/abs/2001.01102v2	https://arxiv.org/pdf/2001.01102v2.pdf	https://github.com/AIRLab-POLIMI/mushroom-rl	true	true	false	tf
https://paperswithcode.com/paper/how-sgd-selects-the-global-minima-in-over	How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective	null	http://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective	http://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective.pdf	https://github.com/leiwu1990/sgd.stability	true	true	false	pytorch
https://paperswithcode.com/paper/extending-text-to-speech-synthesis-with	Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging	2107.05550	https://arxiv.org/abs/2107.05550v1	https://arxiv.org/pdf/2107.05550v1.pdf	https://github.com/BME-SmartLab/txt2ult	true	true	true	tf
https://paperswithcode.com/paper/simulaqron-a-simulator-for-developing-quantum	SimulaQron - A simulator for developing quantum internet software	1712.08032	http://arxiv.org/abs/1712.08032v2	http://arxiv.org/pdf/1712.08032v2.pdf	https://github.com/SoftwareQuTech/CQC-Python	false	false	true	none
https://paperswithcode.com/paper/dgm-a-deep-learning-algorithm-for-solving	DGM: A deep learning algorithm for solving partial differential equations	1708.07469	http://arxiv.org/abs/1708.07469v5	http://arxiv.org/pdf/1708.07469v5.pdf	https://github.com/alialaradi/DeepGalerkinMethod	false	false	true	tf
https://paperswithcode.com/paper/a-spatiotemporal-volumetric-interpolation	A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image	2002.12680	https://arxiv.org/abs/2002.12680v2	https://arxiv.org/pdf/2002.12680v2.pdf	https://github.com/guoyu-niubility/SVIN	true	true	false	pytorch
https://paperswithcode.com/paper/metapruning-meta-learning-for-automatic	MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning	1903.10258	https://arxiv.org/abs/1903.10258v3	https://arxiv.org/pdf/1903.10258v3.pdf	https://github.com/liuzechun/MetaPruning	true	true	true	pytorch
https://paperswithcode.com/paper/variational-adversarial-active-learning	Variational Adversarial Active Learning	1904.00370	https://arxiv.org/abs/1904.00370v3	https://arxiv.org/pdf/1904.00370v3.pdf	https://github.com/sinhasam/vaal	true	true	true	pytorch
https://paperswithcode.com/paper/learning-higher-order-logic-programs	Learning higher-order logic programs	1907.10953	https://arxiv.org/abs/1907.10953v1	https://arxiv.org/pdf/1907.10953v1.pdf	https://github.com/metagol/metagol	true	true	false	none
https://paperswithcode.com/paper/pde-net-learning-pdes-from-data	PDE-Net: Learning PDEs from Data	1710.09668	http://arxiv.org/abs/1710.09668v2	http://arxiv.org/pdf/1710.09668v2.pdf	https://github.com/agrundner24/pde-net-in-tf	false	false	true	tf
https://paperswithcode.com/paper/x-lxmert-paint-caption-and-answer-questions	X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers	2009.11278	https://arxiv.org/abs/2009.11278v1	https://arxiv.org/pdf/2009.11278v1.pdf	https://github.com/allenai/x-lxmert	true	true	false	pytorch
https://paperswithcode.com/paper/infinitygan-towards-infinite-resolution-image	InfinityGAN: Towards Infinite-Pixel Image Synthesis	2104.03963	https://arxiv.org/abs/2104.03963v4	https://arxiv.org/pdf/2104.03963v4.pdf	https://github.com/hubert0527/infinityGAN	true	false	false	pytorch
https://paperswithcode.com/paper/exploring-data-aggregation-in-policy-learning	Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.pdf	https://github.com/autonomousvision/data_aggregation	true	true	false	none
https://paperswithcode.com/paper/graph-structured-prediction-energy-networks	Graph Structured Prediction Energy Networks	1910.14670	https://arxiv.org/abs/1910.14670v2	https://arxiv.org/pdf/1910.14670v2.pdf	https://github.com/cgraber/GSPEN	true	true	false	pytorch
https://paperswithcode.com/paper/image-to-image-translation-with-conditional	Image-to-Image Translation with Conditional Adversarial Networks	1611.07004	http://arxiv.org/abs/1611.07004v3	http://arxiv.org/pdf/1611.07004v3.pdf	https://github.com/sidneykingsley/fyp	false	false	true	tf
https://paperswithcode.com/paper/minibatch-processing-in-spiking-neural	Minibatch Processing in Spiking Neural Networks	1909.02549	https://arxiv.org/abs/1909.02549v1	https://arxiv.org/pdf/1909.02549v1.pdf	https://github.com/djsaunde/snn-minibatch	true	true	false	pytorch
https://paperswithcode.com/paper/stochastic-chebyshev-gradient-descent-for	Stochastic Chebyshev Gradient Descent for Spectral Optimization	1802.06355	http://arxiv.org/abs/1802.06355v3	http://arxiv.org/pdf/1802.06355v3.pdf	https://github.com/EiffL/SpectralFlow	false	false	true	tf
https://paperswithcode.com/paper/learned-image-downscaling-for-upscaling-using	Learned Image Downscaling for Upscaling using Content Adaptive Resampler	1907.12904	https://arxiv.org/abs/1907.12904v2	https://arxiv.org/pdf/1907.12904v2.pdf	https://github.com/twice154/ofa-for-super-resolution	false	false	true	pytorch
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object	Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks	1506.01497	http://arxiv.org/abs/1506.01497v3	http://arxiv.org/pdf/1506.01497v3.pdf	https://github.com/daxiapazi/faster-rcnn	false	false	true	tf
https://paperswithcode.com/paper/every-positive-integer-is-a-sum-of-three	Every positive integer is a sum of three palindromes	1602.06208	http://arxiv.org/abs/1602.06208v2	http://arxiv.org/pdf/1602.06208v2.pdf	https://github.com/TroyLaurin/PalindromeSum	false	false	true	none
https://paperswithcode.com/paper/reducing-the-training-time-of-neural-networks	Reducing the Training Time of Neural Networks by Partitioning	1511.02954	http://arxiv.org/abs/1511.02954v2	http://arxiv.org/pdf/1511.02954v2.pdf	https://github.com/agongt408/vbranch	false	false	true	tf
https://paperswithcode.com/paper/net2net-accelerating-learning-via-knowledge	Net2Net: Accelerating Learning via Knowledge Transfer	1511.05641	http://arxiv.org/abs/1511.05641v4	http://arxiv.org/pdf/1511.05641v4.pdf	https://github.com/agongt408/vbranch	false	false	true	tf
https://paperswithcode.com/paper/simple-non-perturbative-resummation-schemes	Simple non-perturbative resummation schemes beyond mean-field: case study for scalar $φ^4$ theory in 1+1 dimensions	1901.05483	http://arxiv.org/abs/1901.05483v1	http://arxiv.org/pdf/1901.05483v1.pdf	https://github.com/paro8929/Resummation	true	true	true	none
https://paperswithcode.com/paper/the-maven-dependency-graph-a-temporal-graph	The Maven Dependency Graph: a Temporal Graph-based Representation of Maven Central	1901.05392	http://arxiv.org/abs/1901.05392v1	http://arxiv.org/pdf/1901.05392v1.pdf	https://github.com/tdegueul/sonar-dataset	false	false	true	none
https://paperswithcode.com/paper/adversarial-training-methods-for-network	Adversarial Training Methods for Network Embedding	1908.11514	https://arxiv.org/abs/1908.11514v1	https://arxiv.org/pdf/1908.11514v1.pdf	https://github.com/wonniu/AdvT4NE_WWW2019	true	true	false	tf
https://paperswithcode.com/paper/central-server-free-federated-learning-over	Central Server Free Federated Learning over Single-sided Trust Social Networks	1910.04956	https://arxiv.org/abs/1910.04956v2	https://arxiv.org/pdf/1910.04956v2.pdf	https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/standalone/decentralized	true	false	false	pytorch
https://paperswithcode.com/paper/expert-load-matters-operating-networks-at-1	Expert load matters: operating networks at high accuracy and low manual effort	2308.05035	https://arxiv.org/abs/2308.05035v2	https://arxiv.org/pdf/2308.05035v2.pdf	https://github.com/salusanga/aucoc_loss	false	false	true	pytorch
https://paperswithcode.com/paper/accurate-large-minibatch-sgd-training	Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour	1706.02677	http://arxiv.org/abs/1706.02677v2	http://arxiv.org/pdf/1706.02677v2.pdf	https://github.com/darkreapyre/HaaS-dev	false	false	true	tf
https://paperswithcode.com/paper/fiber-cnn-expanding-mask-r-cnn-to-improve	FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis	2006.04552	https://arxiv.org/abs/2006.04552v2	https://arxiv.org/pdf/2006.04552v2.pdf	https://github.com/maxfrei750/synthPIC4Python	true	true	true	none
https://paperswithcode.com/paper/knee-point-identification-based-on-trade-off	Knee Point Identification Based on Trade-Off Utility	2005.11600	https://arxiv.org/abs/2005.11600v1	https://arxiv.org/pdf/2005.11600v1.pdf	https://github.com/COLA-Laboratory/kpi	true	true	false	none
https://paperswithcode.com/paper/matching-networks-for-one-shot-learning	Matching Networks for One Shot Learning	1606.04080	http://arxiv.org/abs/1606.04080v2	http://arxiv.org/pdf/1606.04080v2.pdf	https://github.com/fujenchu/matchingNet	false	false	true	pytorch
https://paperswithcode.com/paper/probabilistic-fasttext-for-multi-sense-word	Probabilistic FastText for Multi-Sense Word Embeddings	1806.02901	http://arxiv.org/abs/1806.02901v1	http://arxiv.org/pdf/1806.02901v1.pdf	https://github.com/benathi/multisense-prob-fasttext	true	true	true	none
https://paperswithcode.com/paper/self-supervised-visual-planning-with-temporal	Self-Supervised Visual Planning with Temporal Skip Connections	1710.05268	http://arxiv.org/abs/1710.05268v1	http://arxiv.org/pdf/1710.05268v1.pdf	https://github.com/CompVis/image2video-synthesis-using-cINNs	false	false	true	pytorch
https://paperswithcode.com/paper/the-hitchhikers-guide-to-lda	The Hitchhiker's Guide to LDA	1908.03142	https://arxiv.org/abs/1908.03142v2	https://arxiv.org/pdf/1908.03142v2.pdf	https://github.com/MachineIntellect/GibbsLDA_plus	false	false	true	none
https://paperswithcode.com/paper/shufflenet-v2-practical-guidelines-for	ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design	1807.11164	http://arxiv.org/abs/1807.11164v1	http://arxiv.org/pdf/1807.11164v1.pdf	https://github.com/savageyusuff/MobilePose-Pi	false	false	true	pytorch
https://paperswithcode.com/paper/unite-unified-translation-evaluation	UniTE: Unified Translation Evaluation	2204.13346	https://arxiv.org/abs/2204.13346v1	https://arxiv.org/pdf/2204.13346v1.pdf	https://github.com/wanyu2018umac/UniTE	true	false	false	pytorch
https://paperswithcode.com/paper/generative-adversarial-networks	Generative Adversarial Networks	1406.2661	https://arxiv.org/abs/1406.2661v1	https://arxiv.org/pdf/1406.2661v1.pdf	https://github.com/etjoa003/medical_imaging	false	false	true	none
https://paperswithcode.com/paper/efficient-nonparametric-statistical-inference	Efficient nonparametric statistical inference on population feature importance using Shapley values	2006.09481	https://arxiv.org/abs/2006.09481v1	https://arxiv.org/pdf/2006.09481v1.pdf	https://github.com/bdwilliamson/spvim_supplementary	false	false	true	none
https://paperswithcode.com/paper/searching-for-mobilenetv3	Searching for MobileNetV3	1905.02244	https://arxiv.org/abs/1905.02244v5	https://arxiv.org/pdf/1905.02244v5.pdf	https://github.com/rwightman/efficientnet-jax	false	false	true	jax
https://paperswithcode.com/paper/wiring-up-vision-minimizing-supervised	Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream	null	https://openreview.net/forum?id=5i4vRgoZauw	https://openreview.net/pdf?id=5i4vRgoZauw	https://github.com/franzigeiger/training_reductions	false	false	false	pytorch
https://paperswithcode.com/paper/coha-ntt-a-configurable-hardware-accelerator	CoHA-NTT: A Configurable Hardware Accelerator for NTT-based Polynomial Multiplication	null	https://eprint.iacr.org/2021/1527	https://eprint.iacr.org/2021/1527.pdf	https://github.com/kemalderya/pqc-param-ntt	false	true	false	none
https://paperswithcode.com/paper/physics-informed-neural-networks-for-non	Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics	2004.04026	https://arxiv.org/abs/2004.04026v2	https://arxiv.org/pdf/2004.04026v2.pdf	https://github.com/jbesty/PINN_system_identification	false	false	true	tf
https://paperswithcode.com/paper/distilling-model-knowledge	Distilling Model Knowledge	1510.02437	http://arxiv.org/abs/1510.02437v1	http://arxiv.org/pdf/1510.02437v1.pdf	https://github.com/gpapamak/distilling_model_knowledge	false	false	true	none
https://paperswithcode.com/paper/effective-obstruction-to-lifting-tate-classes	Effective obstruction to lifting Tate classes from positive characteristic	2003.11037	https://arxiv.org/abs/2003.11037v3	https://arxiv.org/pdf/2003.11037v3.pdf	https://github.com/edgarcosta/crystalline_obstruction	true	true	true	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/StillKeepTry/Transformer-PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/on-the-importance-of-capturing-a-sufficient	On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs	2101.11164	https://arxiv.org/abs/2101.11164v1	https://arxiv.org/pdf/2101.11164v1.pdf	https://github.com/AdamByerly/micro-pcb-analysis	true	false	false	tf
https://paperswithcode.com/paper/dropout-as-a-bayesian-approximation	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	1506.02142	http://arxiv.org/abs/1506.02142v6	http://arxiv.org/pdf/1506.02142v6.pdf	https://github.com/cdebeunne/uncertainties_CNN	false	false	true	pytorch
https://paperswithcode.com/paper/gaussianprocessesjl-a-nonparametric-bayes	GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language	1812.09064	https://arxiv.org/abs/1812.09064v2	https://arxiv.org/pdf/1812.09064v2.pdf	https://github.com/UnofficialJuliaMirrorSnapshots/GaussianProcesses.jl-891a1506-143c-57d2-908e-e1f8e92e6de9	false	false	true	none
https://paperswithcode.com/paper/tractable-higher-order-under-approximating-ae	Tractable higher-order under-approximating AE extensions for non-linear systems	2101.11536	https://arxiv.org/abs/2101.11536v1	https://arxiv.org/pdf/2101.11536v1.pdf	https://github.com/cosynus-lix/RINO	true	false	false	none
https://paperswithcode.com/paper/lightweight-probabilistic-deep-networks	Lightweight Probabilistic Deep Networks	1805.11327	http://arxiv.org/abs/1805.11327v1	http://arxiv.org/pdf/1805.11327v1.pdf	https://github.com/cdebeunne/uncertainties_CNN	false	false	true	pytorch
https://paperswithcode.com/paper/reducing-complexity-and-unidentifiability	Reducing complexity and unidentifiability when modelling human atrial cells	2001.10954	https://arxiv.org/abs/2001.10954v1	https://arxiv.org/pdf/2001.10954v1.pdf	https://github.com/charleshouston/ion-channel-ABC	true	true	false	none
https://paperswithcode.com/paper/model-agnostic-meta-learning-for-fast	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	1703.03400	http://arxiv.org/abs/1703.03400v3	http://arxiv.org/pdf/1703.03400v3.pdf	https://github.com/MoritzTaylor/maml-rl-tf2	false	false	true	tf
https://paperswithcode.com/paper/robust-person-re-identification-by-modelling	Robust Person Re-Identification by Modelling Feature Uncertainty	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.pdf	https://github.com/TianyuanYu/DistributionNet	true	true	false	tf
https://paperswithcode.com/paper/hierarchical-encoding-of-sequential-data-with	Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.pdf	https://github.com/intellhave/HESSL	true	true	false	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.

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.