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 |
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