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/3d-surface-reconstruction-from-multi-date	3D Surface Reconstruction From Multi-Date Satellite Images	2102.02502	https://arxiv.org/abs/2102.02502v2	https://arxiv.org/pdf/2102.02502v2.pdf	https://github.com/SBCV/SatelliteSurfaceReconstruction	true	true	true	none
https://paperswithcode.com/paper/flexible-behavior-trees-in-search-of-the	Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics	2203.05389	https://arxiv.org/abs/2203.05389v1	https://arxiv.org/pdf/2203.05389v1.pdf	https://github.com/flexbe/flex_bt_turtlebot_demo	true	true	false	none
https://paperswithcode.com/paper/foreseeing-brain-graph-evolution-over-time	Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer	2009.11166	https://arxiv.org/abs/2009.11166v1	https://arxiv.org/pdf/2009.11166v1.pdf	https://github.com/basiralab/gGAN	true	true	true	pytorch
https://paperswithcode.com/paper/define-delayed-feedback-based-immersive	DeFINE: Delayed Feedback based Immersive Navigation Environment for Studying Goal-Directed Human Navigation	2003.03133	https://arxiv.org/abs/2003.03133v2	https://arxiv.org/pdf/2003.03133v2.pdf	https://github.com/ktiwari9/define-VR	true	false	false	none
https://paperswithcode.com/paper/efficientnetv2-smaller-models-and-faster	EfficientNetV2: Smaller Models and Faster Training	2104.00298	https://arxiv.org/abs/2104.00298v3	https://arxiv.org/pdf/2104.00298v3.pdf	https://github.com/lukemelas/EfficientNet-PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/ntire-2020-challenge-on-real-world-image	NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results	2005.01996	https://arxiv.org/abs/2005.01996v1	https://arxiv.org/pdf/2005.01996v1.pdf	https://github.com/ArchieMeng/realsr-ncnn-vulkan-python	false	false	true	none
https://paperswithcode.com/paper/scilla-a-smart-contract-intermediate-level	Scilla: a Smart Contract Intermediate-Level LAnguage	1801.00687	https://arxiv.org/abs/1801.00687v1	https://arxiv.org/pdf/1801.00687v1.pdf	https://github.com/pirapira/ethereum-formal-verification-overview	true	true	true	none
https://paperswithcode.com/paper/model-free-price-bounds-under-dynamic-option	Model-free price bounds under dynamic option trading	2101.01024	https://arxiv.org/abs/2101.01024v2	https://arxiv.org/pdf/2101.01024v2.pdf	https://github.com/juliansester/dynamic_option_trading	true	true	true	none
https://paperswithcode.com/paper/tensor-train-density-estimation	Tensor-Train Density Estimation	2108.00089	https://arxiv.org/abs/2108.00089v2	https://arxiv.org/pdf/2108.00089v2.pdf	https://github.com/stat-ml/TTDE	true	true	true	jax
https://paperswithcode.com/paper/sparse-svm-for-sufficient-data-reduction	Sparse SVM for Sufficient Data Reduction	2005.13771	https://arxiv.org/abs/2005.13771v4	https://arxiv.org/pdf/2005.13771v4.pdf	https://github.com/ShenglongZhou/NSSVM	true	false	true	none
https://paperswithcode.com/paper/group-fisher-pruning-for-practical-network	Group Fisher Pruning for Practical Network Compression	2108.00708	https://arxiv.org/abs/2108.00708v1	https://arxiv.org/pdf/2108.00708v1.pdf	https://github.com/jshilong/FisherPruning	true	true	false	pytorch
https://paperswithcode.com/paper/a-general-framework-for-ensemble-distribution	A general framework for ensemble distribution distillation	2002.11531	https://arxiv.org/abs/2002.11531v2	https://arxiv.org/pdf/2002.11531v2.pdf	https://github.com/jackonelli/ensemble_distr_distillation	true	false	true	pytorch
https://paperswithcode.com/paper/a-sketching-framework-for-reduced-data	A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar	2102.08732	https://arxiv.org/abs/2102.08732v4	https://arxiv.org/pdf/2102.08732v4.pdf	https://gitlab.com/Tachella/sketched_lidar	true	true	true	none
https://paperswithcode.com/paper/the-gaussian-neural-process	The Gaussian Neural Process	2101.03606	https://arxiv.org/abs/2101.03606v1	https://arxiv.org/pdf/2101.03606v1.pdf	https://github.com/wesselb/NeuralProcesses.jl	true	true	false	none
https://paperswithcode.com/paper/multiple-attribute-text-style-transfer	Multiple-Attribute Text Style Transfer	1811.00552	https://arxiv.org/abs/1811.00552v2	https://arxiv.org/pdf/1811.00552v2.pdf	https://github.com/facebookresearch/MultipleAttributeTextRewriting	false	false	true	pytorch
https://paperswithcode.com/paper/community-detection-in-sparse-time-evolving	Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian	2006.04510	https://arxiv.org/abs/2006.04510v2	https://arxiv.org/pdf/2006.04510v2.pdf	https://github.com/lorenzodallamico/CoDeBetHe.jl	true	false	false	none
https://paperswithcode.com/paper/logic-consistency-text-generation-from	Logic-Consistency Text Generation from Semantic Parses	2108.00577	https://arxiv.org/abs/2108.00577v1	https://arxiv.org/pdf/2108.00577v1.pdf	https://github.com/Ciaranshu/relogic	true	true	false	pytorch
https://paperswithcode.com/paper/a-unified-framework-for-spectral-clustering	A unified framework for spectral clustering in sparse graphs	2003.09198	https://arxiv.org/abs/2003.09198v2	https://arxiv.org/pdf/2003.09198v2.pdf	https://github.com/lorenzodallamico/CoDeBetHe.jl	true	true	false	none
https://paperswithcode.com/paper/3d-human-mesh-regression-with-dense-1	3D Human Mesh Regression with Dense Correspondence	2006.05734	https://arxiv.org/abs/2006.05734v2	https://arxiv.org/pdf/2006.05734v2.pdf	https://github.com/jiean001/models_m/tree/main/DecoMR	false	false	false	mindspore
https://paperswithcode.com/paper/what-is-the-best-data-augmentation-approach	What is the best data augmentation for 3D brain tumor segmentation?	2010.13372	https://arxiv.org/abs/2010.13372v2	https://arxiv.org/pdf/2010.13372v2.pdf	https://github.com/mdciri/3D-augmentation-techniques	true	true	true	tf
https://paperswithcode.com/paper/distributed-multi-object-tracking-under	Distributed Multi-object Tracking under Limited Field of View Sensors	2012.12990	https://arxiv.org/abs/2012.12990v2	https://arxiv.org/pdf/2012.12990v2.pdf	https://github.com/AdelaideAuto-IDLab/Distributed-limitedFoV-MOT	true	true	true	none
https://paperswithcode.com/paper/automating-involutive-mcmc-using	Automating Involutive MCMC using Probabilistic and Differentiable Programming	2007.09871	https://arxiv.org/abs/2007.09871v2	https://arxiv.org/pdf/2007.09871v2.pdf	https://github.com/probcomp/GenTraceKernelDSL.jl	false	false	true	none
https://paperswithcode.com/paper/optimization-free-test-time-adaptation-for	Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition	2310.18562	https://arxiv.org/abs/2310.18562v2	https://arxiv.org/pdf/2310.18562v2.pdf	https://github.com/Claydon-Wang/OFTTA	true	true	false	pytorch
https://paperswithcode.com/paper/generalized-universe-hierarchies-and-first	Generalized Universe Hierarchies and First-Class Universe Levels	2103.00223	https://arxiv.org/abs/2103.00223v4	https://arxiv.org/pdf/2103.00223v4.pdf	https://github.com/AndrasKovacs/universes	true	true	true	none
https://paperswithcode.com/paper/help-me-identify-is-an-llm-vqa-system-all-we	Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?	2410.13651	https://arxiv.org/abs/2410.13651v1	https://arxiv.org/pdf/2410.13651v1.pdf	https://github.com/shailaja183/objectconceptlearning	true	true	false	pytorch
https://paperswithcode.com/paper/actioncomet-a-zero-shot-approach-to-learn	ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions	2410.13662	https://arxiv.org/abs/2410.13662v1	https://arxiv.org/pdf/2410.13662v1.pdf	https://github.com/shailaja183/actionconceptlearning	true	true	false	pytorch
https://paperswithcode.com/paper/leaf-simulating-large-energy-aware-fog	LEAF: Simulating Large Energy-Aware Fog Computing Environments	2103.01170	https://arxiv.org/abs/2103.01170v1	https://arxiv.org/pdf/2103.01170v1.pdf	https://github.com/dos-group/leaf	true	true	true	none
https://paperswithcode.com/paper/towards-a-quality-metric-for-dense-light	Towards a quality metric for dense light fields	1704.07576	http://arxiv.org/abs/1704.07576v1	http://arxiv.org/pdf/1704.07576v1.pdf	https://github.com/mantiuk/pwcmp	true	true	false	none
https://paperswithcode.com/paper/resa-recurrent-feature-shift-aggregator-for	RESA: Recurrent Feature-Shift Aggregator for Lane Detection	2008.13719	https://arxiv.org/abs/2008.13719v2	https://arxiv.org/pdf/2008.13719v2.pdf	https://github.com/ZJULearning/resa	true	true	true	pytorch
https://paperswithcode.com/paper/exploiting-emotions-for-fake-news-detection	Mining Dual Emotion for Fake News Detection	1903.01728	https://arxiv.org/abs/1903.01728v4	https://arxiv.org/pdf/1903.01728v4.pdf	https://github.com/RMSnow/WWW2021	true	false	true	tf
https://paperswithcode.com/paper/warm-up-cold-start-advertisements-improving	Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings	1904.11547	http://arxiv.org/abs/1904.11547v1	http://arxiv.org/pdf/1904.11547v1.pdf	https://github.com/Feiyang/MetaEmbedding	true	false	false	tf
https://paperswithcode.com/paper/sequential-place-learning-heuristic-free-high	Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition	2103.02074	https://arxiv.org/abs/2103.02074v1	https://arxiv.org/pdf/2103.02074v1.pdf	https://github.com/mchancan/deepseqslam	true	true	true	pytorch
https://paperswithcode.com/paper/fastcat-fast-cone-beam-ct-cbct-simulation	FastCAT: Fast Cone Beam CT (CBCT) Simulation	2011.04736	https://arxiv.org/abs/2011.04736v2	https://arxiv.org/pdf/2011.04736v2.pdf	https://github.com/jerichooconnell/fastCAT	true	true	false	none
https://paperswithcode.com/paper/ensemble-based-learning-of-turbulence-model	Ensemble Kalman method for learning turbulence models from indirect observation data	2202.05122	https://arxiv.org/abs/2202.05122v4	https://arxiv.org/pdf/2202.05122v4.pdf	https://github.com/xiaoh/DAFI	true	true	false	none
https://paperswithcode.com/paper/understanding-the-role-of-momentum-in-non	Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization	2010.00406	https://arxiv.org/abs/2010.00406v4	https://arxiv.org/pdf/2010.00406v4.pdf	https://github.com/facebookresearch/madgrad	true	false	false	pytorch
https://paperswithcode.com/paper/preference-based-learning-for-user-guided-hzd	Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots	2011.05424	https://arxiv.org/abs/2011.05424v2	https://arxiv.org/pdf/2011.05424v2.pdf	https://github.com/maegant/ICRA2021-LearningHZD	true	true	false	none
https://paperswithcode.com/paper/adaptivity-without-compromise-a-momentumized	Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization	2101.11075	https://arxiv.org/abs/2101.11075v3	https://arxiv.org/pdf/2101.11075v3.pdf	https://github.com/facebookresearch/madgrad	true	false	true	pytorch
https://paperswithcode.com/paper/room-classification-on-floor-plan-graphs	Room Classification on Floor Plan Graphs using Graph Neural Networks	2108.05947	https://arxiv.org/abs/2108.05947v1	https://arxiv.org/pdf/2108.05947v1.pdf	https://github.com/abpaudel/floorplan-graph	true	false	true	pytorch
https://paperswithcode.com/paper/a-deep-perceptual-metric-for-3d-point-clouds	A deep perceptual metric for 3D point clouds	2102.12839	https://arxiv.org/abs/2102.12839v1	https://arxiv.org/pdf/2102.12839v1.pdf	https://github.com/mauriceqch/2021_pc_perceptual_loss	true	true	true	tf
https://paperswithcode.com/paper/hamiltonian-simulation-with-random-inputs	Hamiltonian simulation with random inputs	2111.04773	https://arxiv.org/abs/2111.04773v1	https://arxiv.org/pdf/2111.04773v1.pdf	https://github.com/zhaoqthu/hamiltonian-simulation-with-random-inputs	true	true	false	none
https://paperswithcode.com/paper/layer-2-atomic-cross-blockchain-function	Layer 2 Atomic Cross-Blockchain Function Calls	2005.09790	https://arxiv.org/abs/2005.09790v5	https://arxiv.org/pdf/2005.09790v5.pdf	https://github.com/ConsenSys/gpact	false	false	true	none
https://paperswithcode.com/paper/quantifying-covid-19-enforced-global-changes	Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing	2101.03523	https://arxiv.org/abs/2101.03523v3	https://arxiv.org/pdf/2101.03523v3.pdf	https://github.com/manmeet3591/gee_lockdown	true	true	false	none
https://paperswithcode.com/paper/visual-field-prediction-using-recurrent	Visual Field Prediction using Recurrent Neural Network	null	https://www.nature.com/articles/s41598-019-44852-6	https://www.nature.com/articles/s41598-019-44852-6.pdf	https://github.com/mohaEs/VFPrediction	false	false	false	tf
https://paperswithcode.com/paper/a-crash-course-on-reinforcement-learning	A Crash Course on Reinforcement Learning	2103.04910	https://arxiv.org/abs/2103.04910v1	https://arxiv.org/pdf/2103.04910v1.pdf	https://github.com/FarnazAdib/Crash_course_on_RL	true	true	true	tf
https://paperswithcode.com/paper/end-to-end-human-object-interaction-detection	End-to-End Human Object Interaction Detection with HOI Transformer	2103.04503	https://arxiv.org/abs/2103.04503v1	https://arxiv.org/pdf/2103.04503v1.pdf	https://github.com/bbepoch/HoiTransformer	true	true	true	pytorch
https://paperswithcode.com/paper/towards-high-fidelity-face-relighting-with	Towards High Fidelity Face Relighting with Realistic Shadows	2104.00825	https://arxiv.org/abs/2104.00825v2	https://arxiv.org/pdf/2104.00825v2.pdf	https://github.com/andrewhou1/Shadow-Mask-Face-Relighting	true	true	false	tf
https://paperswithcode.com/paper/sdan-squared-deformable-alignment-network-for	SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom	2104.00848	https://arxiv.org/abs/2104.00848v2	https://arxiv.org/pdf/2104.00848v2.pdf	https://github.com/MKFMIKU/SDAN	true	true	false	pytorch
https://paperswithcode.com/paper/lightningdot-pre-training-visual-semantic	LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval	2103.08784	https://arxiv.org/abs/2103.08784v2	https://arxiv.org/pdf/2103.08784v2.pdf	https://github.com/intersun/LightningDOT	true	true	true	pytorch
https://paperswithcode.com/paper/bottom-up-and-top-down-reasoning-with	Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians	1507.05699	http://arxiv.org/abs/1507.05699v5	http://arxiv.org/pdf/1507.05699v5.pdf	https://github.com/peiyunh/rg-mpii	true	true	false	none
https://paperswithcode.com/paper/this-item-is-a-glaxefw-and-this-is-a-glaxuzb	Compositionality Through Language Transmission, using Artificial Neural Networks	2101.11739	https://arxiv.org/abs/2101.11739v2	https://arxiv.org/pdf/2101.11739v2.pdf	https://github.com/asappresearch/neural-ilm	true	true	true	pytorch
https://paperswithcode.com/paper/a-signal-centric-perspective-on-the-evolution	A Signal-Centric Perspective on the Evolution of Symbolic Communication	2103.16882	https://arxiv.org/abs/2103.16882v1	https://arxiv.org/pdf/2103.16882v1.pdf	https://github.com/FraLotito/evol-signal-comm	true	true	false	none
https://paperswithcode.com/paper/trees-forests-chickens-and-eggs-when-and-why	Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest	2103.16700	https://arxiv.org/abs/2103.16700v1	https://arxiv.org/pdf/2103.16700v1.pdf	https://github.com/syzhou5/TreeDepth	true	true	false	none
https://paperswithcode.com/paper/mean-shift-feature-transformer	Mean-Shift Feature Transformer	null	http://openaccess.thecvf.com//content/CVPR2024/html/Kobayashi_Mean-Shift_Feature_Transformer_CVPR_2024_paper.html	http://openaccess.thecvf.com//content/CVPR2024/papers/Kobayashi_Mean-Shift_Feature_Transformer_CVPR_2024_paper.pdf	https://github.com/tk1980/msftransformer	true	true	false	pytorch
https://paperswithcode.com/paper/mediapipe-hands-on-device-real-time-hand	MediaPipe Hands: On-device Real-time Hand Tracking	2006.10214	https://arxiv.org/abs/2006.10214v1	https://arxiv.org/pdf/2006.10214v1.pdf	https://github.com/vidursatija/BlazePalm	false	false	true	pytorch
https://paperswithcode.com/paper/team-phoenix-at-wassa-2021-emotion-analysis	Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models	2103.06057	https://arxiv.org/abs/2103.06057v1	https://arxiv.org/pdf/2103.06057v1.pdf	https://github.com/yashbutala/WASSA	true	true	false	none
https://paperswithcode.com/paper/continuous-weight-balancing	Continuous Weight Balancing	2103.16591	https://arxiv.org/abs/2103.16591v1	https://arxiv.org/pdf/2103.16591v1.pdf	https://github.com/Daniel-Wu/Continuous-Weight-Balancing	true	true	false	none
https://paperswithcode.com/paper/tanksworld-a-multi-agent-environment-for-ai	TanksWorld: A Multi-Agent Environment for AI Safety Research	2002.11174	https://arxiv.org/abs/2002.11174v1	https://arxiv.org/pdf/2002.11174v1.pdf	https://github.com/cgrivera/ai-safety-challenge	false	false	true	tf
https://paperswithcode.com/paper/multi-scale-gcn-assisted-two-stage-network	Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images	2102.04799	https://arxiv.org/abs/2102.04799v1	https://arxiv.org/pdf/2102.04799v1.pdf	https://github.com/Jiaxuan-Li/MGU-Net	true	true	true	pytorch
https://paperswithcode.com/paper/tlsan-time-aware-long-and-short-term-1	TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation	2103.08971	https://arxiv.org/abs/2103.08971v1	https://arxiv.org/pdf/2103.08971v1.pdf	https://github.com/TsingZ0/TLSAN	true	true	false	tf
https://paperswithcode.com/paper/determining-the-maximum-information-gain-and	Determining the maximum information gain and optimising experimental design in neutron reflectometry using the Fisher information	2103.08973	https://arxiv.org/abs/2103.08973v3	https://arxiv.org/pdf/2103.08973v3.pdf	https://github.com/James-Durant/fisher-information	true	true	true	none
https://paperswithcode.com/paper/nonlinear-causal-discovery-via-kernel-anchor	Nonlinear Causal Discovery via Kernel Anchor Regression	2210.16775	https://arxiv.org/abs/2210.16775v1	https://arxiv.org/pdf/2210.16775v1.pdf	https://github.com/swq118/kernel-anchor-regression	true	true	false	none
https://paperswithcode.com/paper/cardiologist-level-arrhythmia-detection-with	Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks	1707.01836	http://arxiv.org/abs/1707.01836v1	http://arxiv.org/pdf/1707.01836v1.pdf	https://github.com/physhik/ecg-mit-bih	false	false	true	tf
https://paperswithcode.com/paper/predicting-pedestrian-crossing-intention-with	Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention	2104.05485	https://arxiv.org/abs/2104.05485v2	https://arxiv.org/pdf/2104.05485v2.pdf	https://github.com/ZeWang95/PedesPred	false	false	true	tf
https://paperswithcode.com/paper/real-time-safety-assessment-of-dynamic	Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques	2304.12583	https://arxiv.org/abs/2304.12583v2	https://arxiv.org/pdf/2304.12583v2.pdf	https://github.com/thufdd/jiaolongdsms_datasets	true	true	false	none
https://paperswithcode.com/paper/ghum-ghuml-generative-3d-human-shape-and	GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_GHUM__GHUML_Generative_3D_Human_Shape_and_Articulated_Pose_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_GHUM__GHUML_Generative_3D_Human_Shape_and_Articulated_Pose_CVPR_2020_paper.pdf	https://github.com/google-research/google-research/tree/master/ghum	true	false	false	tf
https://paperswithcode.com/paper/implicit-normalizing-flows-1	Implicit Normalizing Flows	2103.09527	https://arxiv.org/abs/2103.09527v1	https://arxiv.org/pdf/2103.09527v1.pdf	https://github.com/thu-ml/implicit-normalizing-flows	true	true	true	pytorch
https://paperswithcode.com/paper/hinglishnlp-at-semeval-2020-task-9-fine-tuned	HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection	null	https://aclanthology.org/2020.semeval-1.119	https://aclanthology.org/2020.semeval-1.119.pdf	https://github.com/NirantK/Hinglish	true	true	false	pytorch
https://paperswithcode.com/paper/gated-multimodal-units-for-information-fusion	Gated Multimodal Units for Information Fusion	1702.01992	http://arxiv.org/abs/1702.01992v1	http://arxiv.org/pdf/1702.01992v1.pdf	https://github.com/IsaacRodgz/multimodal-transformers-movies	false	false	true	pytorch
https://paperswithcode.com/paper/non-convex-optimization-for-self-calibration	Non-convex optimization for self-calibration of direction-dependent effects in radio interferometric imaging	1701.03689	http://arxiv.org/abs/1701.03689v2	http://arxiv.org/pdf/1701.03689v2.pdf	https://github.com/basp-group/SARA-CALIB-realdata	false	false	true	none
https://paperswithcode.com/paper/robust-mpc-for-linear-systems-with-parametric	Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach	2007.00930	https://arxiv.org/abs/2007.00930v6	https://arxiv.org/pdf/2007.00930v6.pdf	https://github.com/monimoyb/RMPC_MixedUncertainty	true	true	true	none
https://paperswithcode.com/paper/safely-learning-to-control-the-constrained	Safely Learning to Control the Constrained Linear Quadratic Regulator	1809.10121	https://arxiv.org/abs/1809.10121v2	https://arxiv.org/pdf/1809.10121v2.pdf	https://github.com/monimoyb/RMPC_MixedUncertainty	false	false	true	none
https://paperswithcode.com/paper/discovering-influential-factors-in	Discovering Influential Factors in Variational Autoencoder	1809.01804	http://arxiv.org/abs/1809.01804v2	http://arxiv.org/pdf/1809.01804v2.pdf	https://github.com/647LiuSQ/Discovering-influential-factors-in-variational-autoencoders	true	false	false	tf
https://paperswithcode.com/paper/affordance-transfer-learning-for-human-object	Affordance Transfer Learning for Human-Object Interaction Detection	2104.02867	https://arxiv.org/abs/2104.02867v2	https://arxiv.org/pdf/2104.02867v2.pdf	https://github.com/zhihou7/HOI-CL-OneStage	true	true	true	pytorch
https://paperswithcode.com/paper/visual-compositional-learning-for-human	Visual Compositional Learning for Human-Object Interaction Detection	2007.12407	https://arxiv.org/abs/2007.12407v2	https://arxiv.org/pdf/2007.12407v2.pdf	https://github.com/zhihou7/HOI-CL-OneStage	false	false	true	pytorch
https://paperswithcode.com/paper/evars-gpr-event-triggered-augmented-refitting	EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data	2107.02463	https://arxiv.org/abs/2107.02463v1	https://arxiv.org/pdf/2107.02463v1.pdf	https://github.com/grimmlab/evars-gpr	true	true	false	none
https://paperswithcode.com/paper/relational-gating-for-what-if-reasoning	Relational Gating for "What If" Reasoning	2105.13449	https://arxiv.org/abs/2105.13449v1	https://arxiv.org/pdf/2105.13449v1.pdf	https://github.com/HLR/RGN	true	true	false	pytorch
https://paperswithcode.com/paper/data-centric-semi-supervised-learning	Unsupervised Selective Labeling for More Effective Semi-Supervised Learning	2110.03006	https://arxiv.org/abs/2110.03006v4	https://arxiv.org/pdf/2110.03006v4.pdf	https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling	true	false	true	pytorch
https://paperswithcode.com/paper/linguistic-structures-as-weak-supervision-for	Linguistic Structures as Weak Supervision for Visual Scene Graph Generation	2105.13994	https://arxiv.org/abs/2105.13994v1	https://arxiv.org/pdf/2105.13994v1.pdf	https://github.com/yekeren/WSSGG	true	true	false	tf
https://paperswithcode.com/paper/comprehensive-study-how-the-context	Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?	2105.03571	https://arxiv.org/abs/2105.03571v2	https://arxiv.org/pdf/2105.03571v2.pdf	https://github.com/yangpuhai/Granularity-in-DST	true	true	true	pytorch
https://paperswithcode.com/paper/computing-periodic-points-on-veech-surfaces	Computing Periodic Points on Veech Surfaces	2112.02698	https://arxiv.org/abs/2112.02698v2	https://arxiv.org/pdf/2112.02698v2.pdf	https://github.com/sfreedman67/bowman	true	true	false	none
https://paperswithcode.com/paper/overt-an-algorithm-for-safety-verification-of	OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems	2108.01220	https://arxiv.org/abs/2108.01220v1	https://arxiv.org/pdf/2108.01220v1.pdf	https://github.com/sisl/OVERTVerify.jl	true	true	true	none
https://paperswithcode.com/paper/boosting-weakly-supervised-object-detection-1	Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters	2108.01499	https://arxiv.org/abs/2108.01499v1	https://arxiv.org/pdf/2108.01499v1.pdf	https://github.com/DongSky/lbba_boosted_wsod	true	true	false	pytorch
https://paperswithcode.com/paper/enhancement-of-a-state-of-the-art-rl-based	Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars	2112.02628	https://arxiv.org/abs/2112.02628v2	https://arxiv.org/pdf/2112.02628v2.pdf	https://github.com/lisifra96/improved_rl_algorithm_mmimo_radar	true	true	false	none
https://paperswithcode.com/paper/dialogue-summarization-with-supporting	Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization	2108.01268	https://arxiv.org/abs/2108.01268v1	https://arxiv.org/pdf/2108.01268v1.pdf	https://github.com/Chen-Wang-CUHK/DialSum-with-SUFM-and-FR	true	true	false	pytorch
https://paperswithcode.com/paper/breast-cancer-image-classification-on-wsi	Breast cancer image classification on WSI with spatial correlations	null	https://www.researchgate.net/publication/332790422_Breast_Cancer_Image_Classification_on_WSI_with_Spatial_Correlations?_sg=y0w4GzUN4IH2Q8xVra4yYZwWptcdTEsbyVxAuDTNmT5f5gvlcbpGKI5Ccj-DAgjHtPHJ6NWxpVbPwiUkosEDaaAsKRu_sBOrPQytuF_O.m_8mNN692O90t0LPpasc9b9qVfIP8Wz6jkFgn9Ld5bRPPoMuIK6lEQ93j9hlKrwJRk1folGO0m1Ix7961QKt7g	https://www.researchgate.net/publication/332790422_Breast_Cancer_Image_Classification_on_WSI_with_Spatial_Correlations?_sg=y0w4GzUN4IH2Q8xVra4yYZwWptcdTEsbyVxAuDTNmT5f5gvlcbpGKI5Ccj-DAgjHtPHJ6NWxpVbPwiUkosEDaaAsKRu_sBOrPQytuF_O.m_8mNN692O90t0LPpasc9b9qVfIP8Wz6jkFgn9Ld5bRPPoMuIK6lEQ93j9hlKrwJRk1folGO0m1Ix7961QKt7g	https://github.com/dong100136/Breast-Cancer-Image-Classification-On-WSI-With-Spatial-Correlations	false	false	false	tf
https://paperswithcode.com/paper/meta-pu-an-arbitrary-scale-upsampling-network	Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud	2102.04317	https://arxiv.org/abs/2102.04317v1	https://arxiv.org/pdf/2102.04317v1.pdf	https://github.com/pleaseconnectwifi/Meta-PU	false	false	true	pytorch
https://paperswithcode.com/paper/adapting-membership-inference-attacks-to-gnn	Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications	2110.08760	https://arxiv.org/abs/2110.08760v1	https://arxiv.org/pdf/2110.08760v1.pdf	https://github.com/trustworthygnn/mia-gnn	true	true	true	pytorch
https://paperswithcode.com/paper/a-character-level-decoder-without-explicit	A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation	1603.06147	http://arxiv.org/abs/1603.06147v4	http://arxiv.org/pdf/1603.06147v4.pdf	https://github.com/nyu-dl/dl4mt-cdec	false	false	true	none
https://paperswithcode.com/paper/voicefixer-a-unified-framework-for-high	VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration	2204.05841	https://arxiv.org/abs/2204.05841v2	https://arxiv.org/pdf/2204.05841v2.pdf	https://github.com/haoheliu/voicefixer	true	true	false	pytorch
https://paperswithcode.com/paper/learning-generalized-spoof-cues-for-face-anti	Learning Generalized Spoof Cues for Face Anti-spoofing	2005.03922	https://arxiv.org/abs/2005.03922v1	https://arxiv.org/pdf/2005.03922v1.pdf	https://github.com/mortezagolzan/Face-Anti-Spoofing	false	false	true	paddle
https://paperswithcode.com/paper/detecting-fast-radio-bursts-in-the-milky-way	Detecting Fast Radio Bursts in the Milky Way	2112.02233	https://arxiv.org/abs/2112.02233v1	https://arxiv.org/pdf/2112.02233v1.pdf	https://github.com/cmlflynn/milkyway-frbs	true	true	false	none
https://paperswithcode.com/paper/imagenet-21k-pretraining-for-the-masses	ImageNet-21K Pretraining for the Masses	2104.10972	https://arxiv.org/abs/2104.10972v4	https://arxiv.org/pdf/2104.10972v4.pdf	https://github.com/Alibaba-MIIL/ImageNet21K	true	true	true	pytorch
https://paperswithcode.com/paper/lifting-monocular-events-to-3d-human-poses	Lifting Monocular Events to 3D Human Poses	2104.10609	https://arxiv.org/abs/2104.10609v1	https://arxiv.org/pdf/2104.10609v1.pdf	https://github.com/IIT-PAVIS/lifting_events_to_3d_hpe	true	false	false	pytorch
https://paperswithcode.com/paper/on-convergence-rates-of-adaptive-ensemble	On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems	2104.10895	https://arxiv.org/abs/2104.10895v5	https://arxiv.org/pdf/2104.10895v5.pdf	https://github.com/FabianKP/adaptive_eki	true	true	false	none
https://paperswithcode.com/paper/gender-lost-in-translation-how-bridging-the	Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation	2305.16935	https://arxiv.org/abs/2305.16935v1	https://arxiv.org/pdf/2305.16935v1.pdf	https://github.com/lenacabrera/gb_mnmt	true	true	false	pytorch
https://paperswithcode.com/paper/novel-models-for-multiple-dependent	Novel Models for Multiple Dependent Heteroskedastic Time Series	2310.17760	https://arxiv.org/abs/2310.17760v1	https://arxiv.org/pdf/2310.17760v1.pdf	https://github.com/13204942/stat40710	true	true	false	none
https://paperswithcode.com/paper/kids-1000-methodology-modelling-and-inference	KiDS-1000 Methodology: Modelling and inference for joint weak gravitational lensing and spectroscopic galaxy clustering analysis	2007.01844	https://arxiv.org/abs/2007.01844v2	https://arxiv.org/pdf/2007.01844v2.pdf	https://github.com/kids-wl/cat_to_obs_k1000_p1	false	false	true	none
https://paperswithcode.com/paper/kids-1000-cosmology-multi-probe-weak	KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints	2007.15632	https://arxiv.org/abs/2007.15632v2	https://arxiv.org/pdf/2007.15632v2.pdf	https://github.com/kids-wl/cat_to_obs_k1000_p1	false	false	true	none
https://paperswithcode.com/paper/diverse-beam-search-decoding-diverse	Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models	1610.02424	http://arxiv.org/abs/1610.02424v2	http://arxiv.org/pdf/1610.02424v2.pdf	https://github.com/StatNLP/ada4asr	false	false	true	pytorch
https://paperswithcode.com/paper/a-convnet-for-the-2020s	A ConvNet for the 2020s	2201.03545	https://arxiv.org/abs/2201.03545v2	https://arxiv.org/pdf/2201.03545v2.pdf	https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt	false	false	false	paddle

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.