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/very-deep-convolutional-networks-for-large | Very Deep Convolutional Networks for Large-Scale Image Recognition | 1409.1556 | http://arxiv.org/abs/1409.1556v6 | http://arxiv.org/pdf/1409.1556v6.pdf | https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps | false | false | true | tf |
https://paperswithcode.com/paper/exploring-modern-gpu-memory-system-design | Exploring Modern GPU Memory System Design Challenges through Accurate Modeling | 1810.07269 | http://arxiv.org/abs/1810.07269v1 | http://arxiv.org/pdf/1810.07269v1.pdf | https://github.com/prdalmia/gpgpu-sim-tlb | false | false | true | pytorch |
https://paperswithcode.com/paper/a-simple-exponential-family-framework-for | A Simple Exponential Family Framework for Zero-Shot Learning | 1707.08040 | http://arxiv.org/abs/1707.08040v3 | http://arxiv.org/pdf/1707.08040v3.pdf | https://github.com/vkverma01/Zero-Shot | true | true | true | none |
https://paperswithcode.com/paper/from-word-embeddings-to-item-recommendation | From Word Embeddings to Item Recommendation | 1601.01356 | http://arxiv.org/abs/1601.01356v3 | http://arxiv.org/pdf/1601.01356v3.pdf | https://github.com/mgulcin/DL_Rec | false | false | true | none |
https://paperswithcode.com/paper/190807906 | PCRNet: Point Cloud Registration Network using PointNet Encoding | 1908.07906 | https://arxiv.org/abs/1908.07906v2 | https://arxiv.org/pdf/1908.07906v2.pdf | https://github.com/vinits5/pcrnet_pytorch | true | false | true | pytorch |
https://paperswithcode.com/paper/flavio-a-python-package-for-flavour-and | flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond | 1810.08132 | http://arxiv.org/abs/1810.08132v1 | http://arxiv.org/pdf/1810.08132v1.pdf | https://github.com/smelli/smelli | false | false | true | none |
https://paperswithcode.com/paper/nvisii-a-scriptable-tool-for-photorealistic | NViSII: A Scriptable Tool for Photorealistic Image Generation | 2105.13962 | https://arxiv.org/abs/2105.13962v1 | https://arxiv.org/pdf/2105.13962v1.pdf | https://github.com/owl-project/ViSII | false | false | true | none |
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-text | Very Deep Convolutional Networks for Text Classification | 1606.01781 | http://arxiv.org/abs/1606.01781v2 | http://arxiv.org/pdf/1606.01781v2.pdf | https://github.com/nithishkaviyan/Sentiment-Analysis-of-Yelp-Reviews | false | false | true | pytorch |
https://paperswithcode.com/paper/estimating-seal-pup-production-in-the | Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling | 1808.09254 | https://arxiv.org/abs/1808.09254v2 | https://arxiv.org/pdf/1808.09254v2.pdf | https://github.com/PointProcess/SealCoxProcess-JRSSC-code | false | false | true | none |
https://paperswithcode.com/paper/democratizing-contrastive-language-image-pre | Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision | 2203.05796 | https://arxiv.org/abs/2203.05796v1 | https://arxiv.org/pdf/2203.05796v1.pdf | https://github.com/sense-gvt/declip | true | true | true | pytorch |
https://paperswithcode.com/paper/pose-normalized-image-generation-for-person | Pose-Normalized Image Generation for Person Re-identification | 1712.02225 | http://arxiv.org/abs/1712.02225v6 | http://arxiv.org/pdf/1712.02225v6.pdf | https://github.com/NVlabs/DG-Net | false | false | false | pytorch |
https://paperswithcode.com/paper/simulating-content-consistent-vehicle | Simulating Content Consistent Vehicle Datasets with Attribute Descent | 1912.08855 | https://arxiv.org/abs/1912.08855v2 | https://arxiv.org/pdf/1912.08855v2.pdf | https://github.com/yorkeyao/VehicleX | true | true | true | pytorch |
https://paperswithcode.com/paper/robust-group-synchronization-via-cycle-edge | Robust Group Synchronization via Cycle-Edge Message Passing | 1912.11347 | https://arxiv.org/abs/1912.11347v3 | https://arxiv.org/pdf/1912.11347v3.pdf | https://github.com/yunpeng-shi/CEMP | true | true | false | none |
https://paperswithcode.com/paper/partial-fc-training-10-million-identities-on | Partial FC: Training 10 Million Identities on a Single Machine | 2010.05222 | https://arxiv.org/abs/2010.05222v2 | https://arxiv.org/pdf/2010.05222v2.pdf | https://github.com/JDAI-CV/fast-reid | false | false | false | pytorch |
https://paperswithcode.com/paper/tab2know-building-a-knowledge-base-from | Tab2Know: Building a Knowledge Base from Tables in Scientific Papers | 2107.13306 | https://arxiv.org/abs/2107.13306v1 | https://arxiv.org/pdf/2107.13306v1.pdf | https://github.com/karmaresearch/tab2know | true | true | false | none |
https://paperswithcode.com/paper/inference-and-forecasting-for-continuous-time | Inference and forecasting for continuous-time integer-valued trawl processes | 2107.03674 | https://arxiv.org/abs/2107.03674v3 | https://arxiv.org/pdf/2107.03674v3.pdf | https://github.com/mbennedsen/Likelihood-based-IVT | true | true | false | none |
https://paperswithcode.com/paper/mintrec2-0-a-large-scale-benchmark-dataset | MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations | 2403.10943 | https://arxiv.org/abs/2403.10943v4 | https://arxiv.org/pdf/2403.10943v4.pdf | https://github.com/thuiar/mintrec2.0 | true | true | false | pytorch |
https://paperswithcode.com/paper/faultnet-a-deep-convolutional-neural-network | FaultNet: A Deep Convolutional Neural Network for bearing fault classification | 2010.02146 | https://arxiv.org/abs/2010.02146v2 | https://arxiv.org/pdf/2010.02146v2.pdf | https://github.com/BaratiLab/FaultNet | true | true | true | none |
https://paperswithcode.com/paper/physics-informed-neural-networks-for-power | Physics-Informed Neural Networks for Power Systems | 1911.03737 | https://arxiv.org/abs/1911.03737v3 | https://arxiv.org/pdf/1911.03737v3.pdf | https://github.com/gmisy/Phycics-informed-NN-for-Power-Systems | false | false | true | tf |
https://paperswithcode.com/paper/proximal-policy-optimization-algorithms | Proximal Policy Optimization Algorithms | 1707.06347 | http://arxiv.org/abs/1707.06347v2 | http://arxiv.org/pdf/1707.06347v2.pdf | https://github.com/jfpettit/flare | false | false | true | pytorch |
https://paperswithcode.com/paper/susy-les-houches-accord-2 | SUSY Les Houches Accord 2 | 0801.0045 | http://arxiv.org/abs/0801.0045v3 | http://arxiv.org/pdf/0801.0045v3.pdf | https://github.com/misho104/yaslha | false | false | true | none |
https://paperswithcode.com/paper/a-guide-to-convolution-arithmetic-for-deep | A guide to convolution arithmetic for deep learning | 1603.07285 | http://arxiv.org/abs/1603.07285v2 | http://arxiv.org/pdf/1603.07285v2.pdf | https://github.com/marbleton/FPGA_MNIST | false | false | true | pytorch |
https://paperswithcode.com/paper/don-t-stop-pretraining-adapt-language-models | Don't Stop Pretraining: Adapt Language Models to Domains and Tasks | 2004.10964 | https://arxiv.org/abs/2004.10964v3 | https://arxiv.org/pdf/2004.10964v3.pdf | https://github.com/allenai/dont-stop-pretraining | true | true | false | pytorch |
https://paperswithcode.com/paper/hinglishnlp-fine-tuned-language-models-for | HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection | 2008.09820 | https://arxiv.org/abs/2008.09820v1 | https://arxiv.org/pdf/2008.09820v1.pdf | https://github.com/NirantK/Hinglish | true | true | false | pytorch |
https://paperswithcode.com/paper/a-low-cost-flexible-and-portable-volumetric | A Low-Cost, Flexible and Portable Volumetric Capturing System | 1909.01207 | https://arxiv.org/abs/1909.01207v1 | https://arxiv.org/pdf/1909.01207v1.pdf | https://github.com/VCL3D/VolumetricCapture | true | true | true | none |
https://paperswithcode.com/paper/deep-soft-procrustes-for-markerless | Deep Soft Procrustes for Markerless Volumetric Sensor Alignment | 2003.10176 | https://arxiv.org/abs/2003.10176v1 | https://arxiv.org/pdf/2003.10176v1.pdf | https://github.com/VCL3D/VolumetricCapture | false | false | true | none |
https://paperswithcode.com/paper/conversations-with-search-engines | Conversations with Search Engines: SERP-based Conversational Response Generation | 2004.14162 | https://arxiv.org/abs/2004.14162v2 | https://arxiv.org/pdf/2004.14162v2.pdf | https://github.com/PengjieRen/CaSE-1.0 | true | true | false | pytorch |
https://paperswithcode.com/paper/boilerplate-removal-using-a-neural-sequence | Boilerplate Removal using a Neural Sequence Labeling Model | 2004.14294 | https://arxiv.org/abs/2004.14294v1 | https://arxiv.org/pdf/2004.14294v1.pdf | https://github.com/mrjleo/boilernet | true | true | false | tf |
https://paperswithcode.com/paper/lifelong-learning-in-evolving-graphs-with | Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes | 2112.10558 | https://arxiv.org/abs/2112.10558v2 | https://arxiv.org/pdf/2112.10558v2.pdf | https://github.com/lgalke/lifelong-learning | true | true | false | pytorch |
https://paperswithcode.com/paper/towards-efficient-covid-19-ct-annotation-a | Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation | 2004.12537 | https://arxiv.org/abs/2004.12537v2 | https://arxiv.org/pdf/2004.12537v2.pdf | https://github.com/JunMa11/COVID-19-CT-Seg-Benchmark | false | false | true | none |
https://paperswithcode.com/paper/jack-the-reader-a-machine-reading-framework | Jack the Reader - A Machine Reading Framework | 1806.08727 | http://arxiv.org/abs/1806.08727v1 | http://arxiv.org/pdf/1806.08727v1.pdf | https://github.com/uclnlp/jack | false | false | true | tf |
https://paperswithcode.com/paper/making-neural-qa-as-simple-as-possible-but | Making Neural QA as Simple as Possible but not Simpler | 1703.04816 | http://arxiv.org/abs/1703.04816v3 | http://arxiv.org/pdf/1703.04816v3.pdf | https://github.com/uclnlp/jack | false | false | true | tf |
https://paperswithcode.com/paper/a-disentangling-invertible-interpretation | A Disentangling Invertible Interpretation Network for Explaining Latent Representations | 2004.13166 | https://arxiv.org/abs/2004.13166v1 | https://arxiv.org/pdf/2004.13166v1.pdf | https://github.com/CompVis/iin | false | false | true | pytorch |
https://paperswithcode.com/paper/weakly-supervised-open-retrieval | Weakly-Supervised Open-Retrieval Conversational Question Answering | 2103.02537 | https://arxiv.org/abs/2103.02537v1 | https://arxiv.org/pdf/2103.02537v1.pdf | https://github.com/prdwb/ws-orconvqa | true | true | false | pytorch |
https://paperswithcode.com/paper/aggregate-hardware-impairments-over-mixed-rf | Aggregate Hardware Impairments Over Mixed RF/FSO Relaying Systems With Outdated CSI | 1902.03177 | http://arxiv.org/abs/1902.03177v1 | http://arxiv.org/pdf/1902.03177v1.pdf | https://github.com/ebalti/Malaga-Distribution | true | false | false | none |
https://paperswithcode.com/paper/conditional-generative-adversarial-nets | Conditional Generative Adversarial Nets | 1411.1784 | https://arxiv.org/abs/1411.1784v1 | https://arxiv.org/pdf/1411.1784v1.pdf | https://github.com/bhiziroglu/Conditional-Generative-Adversarial-Network | false | false | true | pytorch |
https://paperswithcode.com/paper/inverse-kinematics-for-serial-kinematic | Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization | 1909.09318 | https://arxiv.org/abs/1909.09318v2 | https://arxiv.org/pdf/1909.09318v2.pdf | https://github.com/utiasSTARS/sos-ik | true | true | true | none |
https://paperswithcode.com/paper/how-low-is-too-low-a-computational | How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages | 2105.14515 | https://arxiv.org/abs/2105.14515v1 | https://arxiv.org/pdf/2105.14515v1.pdf | https://github.com/cdli-gh/Semi-Supervised-NMT-for-Sumerian-English | true | false | false | pytorch |
https://paperswithcode.com/paper/voxlingua107-a-dataset-for-spoken-language-1 | VoxLingua107: a Dataset for Spoken Language Recognition | 2011.12998 | https://arxiv.org/abs/2011.12998v1 | https://arxiv.org/pdf/2011.12998v1.pdf | https://github.com/rush-d/spoken-language-identification | false | false | true | none |
https://paperswithcode.com/paper/warpgan-automatic-caricature-generation | WarpGAN: Automatic Caricature Generation | 1811.10100 | http://arxiv.org/abs/1811.10100v3 | http://arxiv.org/pdf/1811.10100v3.pdf | https://github.com/ronny3050/AdvFaces | false | false | true | tf |
https://paperswithcode.com/paper/advfaces-adversarial-face-synthesis | AdvFaces: Adversarial Face Synthesis | 1908.05008 | https://arxiv.org/abs/1908.05008v1 | https://arxiv.org/pdf/1908.05008v1.pdf | https://github.com/ronny3050/AdvFaces | false | false | true | tf |
https://paperswithcode.com/paper/segan-speech-enhancement-generative | SEGAN: Speech Enhancement Generative Adversarial Network | 1703.09452 | http://arxiv.org/abs/1703.09452v3 | http://arxiv.org/pdf/1703.09452v3.pdf | https://github.com/usimarit/TiramisuASR | false | false | true | tf |
https://paperswithcode.com/paper/simple-scalable-and-stable-variational-deep | Simple, Scalable, and Stable Variational Deep Clustering | 2005.08047 | https://arxiv.org/abs/2005.08047v2 | https://arxiv.org/pdf/2005.08047v2.pdf | https://github.com/king/s3vdc | true | true | true | tf |
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style | A Neural Algorithm of Artistic Style | 1508.06576 | http://arxiv.org/abs/1508.06576v2 | http://arxiv.org/pdf/1508.06576v2.pdf | https://github.com/kanishk16/Image-Style-Transfer | false | false | true | pytorch |
https://paperswithcode.com/paper/a-mathematical-formalization-of-hierarchical | A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler | 1601.06116 | http://arxiv.org/abs/1601.06116v3 | http://arxiv.org/pdf/1601.06116v3.pdf | https://github.com/mrkrynmdsco/htm-python | false | false | true | none |
https://paperswithcode.com/paper/density-encoding-enables-resource-efficient | Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks | 1909.09153 | https://arxiv.org/abs/1909.09153v2 | https://arxiv.org/pdf/1909.09153v2.pdf | https://github.com/sweetwenwen/Stochastic-computing-based-neural-network-accelerator | false | false | true | none |
https://paperswithcode.com/paper/a-distance-preserving-matrix-sketch | A Distance-preserving Matrix Sketch | 2009.03979 | https://arxiv.org/abs/2009.03979v3 | https://arxiv.org/pdf/2009.03979v3.pdf | https://github.com/hrluo/DistancePreservingMatrixSketch | true | true | true | none |
https://paperswithcode.com/paper/neural-collaborative-filtering | Neural Collaborative Filtering | 1708.05031 | http://arxiv.org/abs/1708.05031v2 | http://arxiv.org/pdf/1708.05031v2.pdf | https://github.com/EdoardoPona/Neural-Collaborative-Filtering | false | false | true | pytorch |
https://paperswithcode.com/paper/jobskape-a-framework-for-generating-synthetic | JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching | 2402.03242 | https://arxiv.org/abs/2402.03242v1 | https://arxiv.org/pdf/2402.03242v1.pdf | https://github.com/magantoine/jobskape | true | true | true | pytorch |
https://paperswithcode.com/paper/practical-graph-isomorphism-ii | Practical graph isomorphism, II | 1301.1493 | http://arxiv.org/abs/1301.1493v1 | http://arxiv.org/pdf/1301.1493v1.pdf | https://github.com/Mith13/Graphs-isomorphism | false | false | true | none |
https://paperswithcode.com/paper/line-large-scale-information-network | LINE: Large-scale Information Network Embedding | 1503.03578 | http://arxiv.org/abs/1503.03578v1 | http://arxiv.org/pdf/1503.03578v1.pdf | https://github.com/2myeonggyu/Graph-Embedding | false | false | true | none |
https://paperswithcode.com/paper/imagenet-classification-with-deep | ImageNet Classification with Deep Convolutional Neural Networks | null | http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks | http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf | https://github.com/mindspore-courses/heads-on-mindspore/blob/main/1-best-practice/models/alexnet.py | false | false | false | mindspore |
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/muramasa8191/DeepLearning | false | false | true | tf |
https://paperswithcode.com/paper/instance-based-counterfactual-explanations | Instance-based Counterfactual Explanations for Time Series Classification | 2009.13211 | https://arxiv.org/abs/2009.13211v2 | https://arxiv.org/pdf/2009.13211v2.pdf | https://github.com/e-delaney/Instance-Based_CFE_TSC | true | true | false | tf |
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | 1810.04805 | https://arxiv.org/abs/1810.04805v2 | https://arxiv.org/pdf/1810.04805v2.pdf | https://github.com/Holldean/BERT-Pruning | false | false | true | tf |
https://paperswithcode.com/paper/structured-pruning-of-large-language-models | Structured Pruning of Large Language Models | 1910.04732 | https://arxiv.org/abs/1910.04732v2 | https://arxiv.org/pdf/1910.04732v2.pdf | https://github.com/Holldean/BERT-Pruning | false | false | true | tf |
https://paperswithcode.com/paper/pretraining-based-natural-language-generation | Pretraining-Based Natural Language Generation for Text Summarization | 1902.09243 | http://arxiv.org/abs/1902.09243v2 | http://arxiv.org/pdf/1902.09243v2.pdf | https://github.com/praveenjune17/BERT_text_summarisation | false | false | true | tf |
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | 1810.04805 | https://arxiv.org/abs/1810.04805v2 | https://arxiv.org/pdf/1810.04805v2.pdf | https://github.com/Zehui127/SQUAD_BERT | false | false | true | tf |
https://paperswithcode.com/paper/sdf-srn-learning-signed-distance-3d-object | SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images | 2010.10505 | https://arxiv.org/abs/2010.10505v1 | https://arxiv.org/pdf/2010.10505v1.pdf | https://github.com/chenhsuanlin/signed-distance-SRN | true | false | true | pytorch |
https://paperswithcode.com/paper/multifit-efficient-multi-lingual-language | MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 1909.04761 | https://arxiv.org/abs/1909.04761v2 | https://arxiv.org/pdf/1909.04761v2.pdf | https://github.com/n-waves/multifit | false | false | true | none |
https://paperswithcode.com/paper/rtfm-generalising-to-novel-environment | RTFM: Generalising to Novel Environment Dynamics via Reading | 1910.08210 | https://arxiv.org/abs/1910.08210v6 | https://arxiv.org/pdf/1910.08210v6.pdf | https://github.com/facebookresearch/RTFM | false | false | true | none |
https://paperswithcode.com/paper/what-are-people-asking-about-covid-19-a | What Are People Asking About COVID-19? A Question Classification Dataset | 2005.12522 | https://arxiv.org/abs/2005.12522v3 | https://arxiv.org/pdf/2005.12522v3.pdf | https://github.com/JerryWei03/COVID-Q | true | true | true | none |
https://paperswithcode.com/paper/gnn3dmot-graph-neural-network-for-3d-multi | GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.pdf | https://github.com/xinshuoweng/GNN3DMOT | true | true | false | pytorch |
https://paperswithcode.com/paper/mpnet-masked-and-permuted-pre-training-for | MPNet: Masked and Permuted Pre-training for Language Understanding | 2004.09297 | https://arxiv.org/abs/2004.09297v2 | https://arxiv.org/pdf/2004.09297v2.pdf | https://github.com/JunnYu/paddle-mpnet | false | false | false | paddle |
https://paperswithcode.com/paper/logical-inference-for-counting-on-semi | Logical Inference for Counting on Semi-structured Tables | 2204.07803 | https://arxiv.org/abs/2204.07803v2 | https://arxiv.org/pdf/2204.07803v2.pdf | https://github.com/ynklab/sst_count | true | true | false | none |
https://paperswithcode.com/paper/contrastive-learning-with-hard-negative | Contrastive Learning with Hard Negative Entities for Entity Set Expansion | 2204.07789 | https://arxiv.org/abs/2204.07789v2 | https://arxiv.org/pdf/2204.07789v2.pdf | https://github.com/geekjuruo/probexpan | true | true | false | pytorch |
https://paperswithcode.com/paper/reordering-examples-helps-during-priming | Reordering Examples Helps during Priming-based Few-Shot Learning | 2106.01751 | https://arxiv.org/abs/2106.01751v1 | https://arxiv.org/pdf/2106.01751v1.pdf | https://github.com/SawanKumar28/pero | true | true | false | pytorch |
https://paperswithcode.com/paper/conversational-neuro-symbolic-commonsense | Conversational Neuro-Symbolic Commonsense Reasoning | 2006.10022 | https://arxiv.org/abs/2006.10022v3 | https://arxiv.org/pdf/2006.10022v3.pdf | https://github.com/ForoughA/CORGI | true | true | true | pytorch |
https://paperswithcode.com/paper/depth-aware-video-frame-interpolation | Depth-Aware Video Frame Interpolation | 1904.00830 | http://arxiv.org/abs/1904.00830v1 | http://arxiv.org/pdf/1904.00830v1.pdf | https://github.com/BurguerJohn/Dain-App | false | false | false | pytorch |
https://paperswithcode.com/paper/planning-to-explore-via-self-supervised-world | Planning to Explore via Self-Supervised World Models | 2005.05960 | https://arxiv.org/abs/2005.05960v2 | https://arxiv.org/pdf/2005.05960v2.pdf | https://github.com/ramanans1/plan2explore | true | false | false | tf |
https://paperswithcode.com/paper/quota-based-debiasing-can-decrease | Quota-based debiasing can decrease representation of already underrepresented groups | 2006.07647 | https://arxiv.org/abs/2006.07647v1 | https://arxiv.org/pdf/2006.07647v1.pdf | https://github.com/ibsmirnov/debiasing | true | true | true | none |
https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object | EfficientDet: Scalable and Efficient Object Detection | 1911.09070 | https://arxiv.org/abs/1911.09070v7 | https://arxiv.org/pdf/1911.09070v7.pdf | https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker | false | false | true | pytorch |
https://paperswithcode.com/paper/selection-bias-tracking-and-detailed-subset | Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data | 1906.07625 | https://arxiv.org/abs/1906.07625v2 | https://arxiv.org/pdf/1906.07625v2.pdf | https://github.com/VACLab/CadenceEVA | false | false | true | none |
https://paperswithcode.com/paper/simple-online-and-realtime-tracking-with-a | Simple Online and Realtime Tracking with a Deep Association Metric | 1703.07402 | http://arxiv.org/abs/1703.07402v1 | http://arxiv.org/pdf/1703.07402v1.pdf | https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker | false | false | true | pytorch |
https://paperswithcode.com/paper/pseudo-labeling-and-confirmation-bias-in-deep | Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning | 1908.02983 | https://arxiv.org/abs/1908.02983v5 | https://arxiv.org/pdf/1908.02983v5.pdf | https://github.com/EricArazo/PseudoLabeling | false | false | true | pytorch |
https://paperswithcode.com/paper/selective-kernel-networks | Selective Kernel Networks | 1903.06586 | http://arxiv.org/abs/1903.06586v2 | http://arxiv.org/pdf/1903.06586v2.pdf | https://github.com/implus/PytorchInsight | false | false | true | pytorch |
https://paperswithcode.com/paper/post-hoc-methods-for-debiasing-neural | Intra-Processing Methods for Debiasing Neural Networks | 2006.08564 | https://arxiv.org/abs/2006.08564v2 | https://arxiv.org/pdf/2006.08564v2.pdf | https://github.com/realityengines/post_hoc_debiasing | true | true | true | pytorch |
https://paperswithcode.com/paper/deep-packet-a-novel-approach-for-encrypted | Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning | 1709.02656 | http://arxiv.org/abs/1709.02656v3 | http://arxiv.org/pdf/1709.02656v3.pdf | https://github.com/mhwong2007/Deep-Packet | false | false | false | pytorch |
https://paperswithcode.com/paper/robust-differentially-private-training-of | On the effect of normalization layers on Differentially Private training of deep Neural networks | 2006.10919 | https://arxiv.org/abs/2006.10919v2 | https://arxiv.org/pdf/2006.10919v2.pdf | https://github.com/uds-lsv/SIDP | true | true | true | pytorch |
https://paperswithcode.com/paper/neural-combinatorial-optimization-with | Neural Combinatorial Optimization with Reinforcement Learning | 1611.09940 | http://arxiv.org/abs/1611.09940v3 | http://arxiv.org/pdf/1611.09940v3.pdf | https://github.com/Rintarooo/TSP_DRL_PointerNet | false | false | true | pytorch |
https://paperswithcode.com/paper/pointer-networks | Pointer Networks | 1506.03134 | http://arxiv.org/abs/1506.03134v2 | http://arxiv.org/pdf/1506.03134v2.pdf | https://github.com/Rintarooo/TSP_DRL_PointerNet | false | false | true | pytorch |
https://paperswithcode.com/paper/oops-predicting-unintentional-action-in-video | Oops! Predicting Unintentional Action in Video | 1911.11206 | https://arxiv.org/abs/1911.11206v1 | https://arxiv.org/pdf/1911.11206v1.pdf | https://github.com/cvlab-columbia/oops | false | false | false | pytorch |
https://paperswithcode.com/paper/mining-persistent-activity-in-continually | Mining Persistent Activity in Continually Evolving Networks | 2006.15410 | https://arxiv.org/abs/2006.15410v1 | https://arxiv.org/pdf/2006.15410v1.pdf | https://github.com/GemsLab/PENminer | true | true | false | 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/AssafSinger94/sigmorphon-2020-inflection | false | false | true | pytorch |
https://paperswithcode.com/paper/modeling-relational-data-with-graph | Modeling Relational Data with Graph Convolutional Networks | 1703.06103 | http://arxiv.org/abs/1703.06103v4 | http://arxiv.org/pdf/1703.06103v4.pdf | https://github.com/INK-USC/MHGRN | false | false | true | pytorch |
https://paperswithcode.com/paper/kagnet-knowledge-aware-graph-networks-for | KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning | 1909.02151 | https://arxiv.org/abs/1909.02151v1 | https://arxiv.org/pdf/1909.02151v1.pdf | https://github.com/INK-USC/MHGRN | false | false | true | pytorch |
https://paperswithcode.com/paper/toward-the-first-quantum-simulation-with | Toward the first quantum simulation with quantum speedup | 1711.10980 | http://arxiv.org/abs/1711.10980v1 | http://arxiv.org/pdf/1711.10980v1.pdf | https://github.com/njross/simcount | true | true | true | none |
https://paperswithcode.com/paper/port-hamiltonian-approach-to-neural-network | Port-Hamiltonian Approach to Neural Network Training | 1909.02702 | https://arxiv.org/abs/1909.02702v1 | https://arxiv.org/pdf/1909.02702v1.pdf | https://github.com/esclear/ph-nn | false | false | true | tf |
https://paperswithcode.com/paper/the-shapley-value-of-coalition-of-variables | The Shapley Value of coalition of variables provides better explanations | 2103.13342 | https://arxiv.org/abs/2103.13342v3 | https://arxiv.org/pdf/2103.13342v3.pdf | https://github.com/salimamoukou/acv00 | true | true | false | none |
https://paperswithcode.com/paper/gspn-generative-shape-proposal-network-for-3d | GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud | 1812.03320 | http://arxiv.org/abs/1812.03320v1 | http://arxiv.org/pdf/1812.03320v1.pdf | https://github.com/ericyi/GSPN | true | false | true | tf |
https://paperswithcode.com/paper/yolact-better-real-time-instance-segmentation | YOLACT++: Better Real-time Instance Segmentation | 1912.06218 | https://arxiv.org/abs/1912.06218v2 | https://arxiv.org/pdf/1912.06218v2.pdf | https://github.com/2023-MindSpore-1/ms-code-217/tree/main/Yolact%2B%2B | false | false | false | mindspore |
https://paperswithcode.com/paper/feature-pyramid-networks-for-object-detection | Feature Pyramid Networks for Object Detection | 1612.03144 | http://arxiv.org/abs/1612.03144v2 | http://arxiv.org/pdf/1612.03144v2.pdf | https://github.com/daxiapazi/faster-rcnn | false | false | true | tf |
https://paperswithcode.com/paper/yolo9000-better-faster-stronger | YOLO9000: Better, Faster, Stronger | 1612.08242 | http://arxiv.org/abs/1612.08242v1 | http://arxiv.org/pdf/1612.08242v1.pdf | https://github.com/TheClub4/car-detection-yolov2 | false | false | true | tf |
https://paperswithcode.com/paper/image-style-transfer-using-convolutional | Image Style Transfer Using Convolutional Neural Networks | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Gatys_Image_Style_Transfer_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf | https://github.com/gsurma/style_transfer/blob/master/style-transfer.ipynb | false | false | false | none |
https://paperswithcode.com/paper/pic-permutation-invariant-critic-for-multi | PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning | 1911.00025 | https://arxiv.org/abs/1911.00025v1 | https://arxiv.org/pdf/1911.00025v1.pdf | https://github.com/IouJenLiu/PIC | true | true | true | pytorch |
https://paperswithcode.com/paper/efficient-graph-similarity-computation-with | Efficient Graph Similarity Computation with Alignment Regularization | 2406.14929 | https://arxiv.org/abs/2406.14929v1 | https://arxiv.org/pdf/2406.14929v1.pdf | https://github.com/jhuow/eric | true | true | false | pytorch |
https://paperswithcode.com/paper/validations-and-corrections-of-the-sfd-and | Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data | 2204.01521 | https://arxiv.org/abs/2204.01521v3 | https://arxiv.org/pdf/2204.01521v3.pdf | https://github.com/qy-sunyang/extinction-maps-correction | true | true | true | none |
https://paperswithcode.com/paper/fake-review-detection-using-behavioral-and | Fake Review Detection Using Behavioral and Contextual Features | 2003.00807 | https://arxiv.org/abs/2003.00807v1 | https://arxiv.org/pdf/2003.00807v1.pdf | https://github.com/JayKumarr/Fake-Review-Detection | false | false | true | none |
https://paperswithcode.com/paper/assd-attentive-single-shot-multibox-detector | ASSD: Attentive Single Shot Multibox Detector | 1909.12456 | https://arxiv.org/abs/1909.12456v1 | https://arxiv.org/pdf/1909.12456v1.pdf | https://github.com/yijingru/ASSD-Pytorch | true | true | true | pytorch |
https://paperswithcode.com/paper/deep-inside-convolutional-networks | Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps | 1312.6034 | http://arxiv.org/abs/1312.6034v2 | http://arxiv.org/pdf/1312.6034v2.pdf | https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps | false | false | true | tf |
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