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/conditional-neural-processes | Conditional Neural Processes | 1807.01613 | http://arxiv.org/abs/1807.01613v1 | http://arxiv.org/pdf/1807.01613v1.pdf | https://github.com/wesselb/NeuralProcesses.jl | false | false | true | none |
https://paperswithcode.com/paper/supervised-multimodal-bitransformers-for | Supervised Multimodal Bitransformers for Classifying Images and Text | 1909.02950 | https://arxiv.org/abs/1909.02950v2 | https://arxiv.org/pdf/1909.02950v2.pdf | https://github.com/IsaacRodgz/multimodal-transformers-movies | false | false | true | pytorch |
https://paperswithcode.com/paper/adversarial-audio-synthesis | Adversarial Audio Synthesis | 1802.04208 | http://arxiv.org/abs/1802.04208v3 | http://arxiv.org/pdf/1802.04208v3.pdf | https://github.com/MaxHolmberg96/WaveGAN | false | false | true | tf |
https://paperswithcode.com/paper/towards-faster-reasoners-by-using-transparent | Towards Faster Reasoners By Using Transparent Huge Pages | 2004.14378 | https://arxiv.org/abs/2004.14378v1 | https://arxiv.org/pdf/2004.14378v1.pdf | https://github.com/daajoe/thp_docker_build | false | false | true | none |
https://paperswithcode.com/paper/challenging-euclidean-topological | Challenging Euclidean Topological Autoencoders | null | https://openreview.net/forum?id=P3dZuOUnyEY | https://openreview.net/pdf?id=P3dZuOUnyEY | https://github.com/BorgwardtLab/topo-ae-distances | true | true | false | pytorch |
https://paperswithcode.com/paper/yolov3-an-incremental-improvement | YOLOv3: An Incremental Improvement | 1804.02767 | http://arxiv.org/abs/1804.02767v1 | http://arxiv.org/pdf/1804.02767v1.pdf | https://github.com/harsh2011/Yolov3-Detector | false | false | true | pytorch |
https://paperswithcode.com/paper/high-quality-monocular-depth-estimation-via | High Quality Monocular Depth Estimation via Transfer Learning | 1812.11941 | http://arxiv.org/abs/1812.11941v2 | http://arxiv.org/pdf/1812.11941v2.pdf | https://github.com/Noopuragr/DepthModel | false | false | true | tf |
https://paperswithcode.com/paper/tednet-a-pytorch-toolkit-for-tensor | TedNet: A Pytorch Toolkit for Tensor Decomposition Networks | 2104.05018 | https://arxiv.org/abs/2104.05018v2 | https://arxiv.org/pdf/2104.05018v2.pdf | https://github.com/tnbar/tednet | true | true | true | pytorch |
https://paperswithcode.com/paper/annual-modulations-from-secular-variations | Annual modulations from secular variations: not relaxing DAMA? | 2003.03340 | https://arxiv.org/abs/2003.03340v2 | https://arxiv.org/pdf/2003.03340v2.pdf | https://github.com/piacent/bayes_analysis | true | true | true | none |
https://paperswithcode.com/paper/task-programming-learning-data-efficient | Task Programming: Learning Data Efficient Behavior Representations | 2011.13917 | https://arxiv.org/abs/2011.13917v2 | https://arxiv.org/pdf/2011.13917v2.pdf | https://github.com/neuroethology/TREBA | true | true | true | pytorch |
https://paperswithcode.com/paper/cubic-function-fields-with-prescribed | Cubic function fields with prescribed ramification | 2003.06673 | https://arxiv.org/abs/2003.06673v2 | https://arxiv.org/pdf/2003.06673v2.pdf | https://github.com/JRSijsling/parshin_experiments | true | true | true | none |
https://paperswithcode.com/paper/can-multi-label-classification-networks-know-1 | Can multi-label classification networks know what they don’t know? | null | https://openreview.net/forum?id=enKhMfthDFS | https://openreview.net/pdf?id=enKhMfthDFS | https://github.com/deeplearning-wisc/multi-label-ood | true | true | false | pytorch |
https://paperswithcode.com/paper/general-audio-tagging-with-ensembling | General audio tagging with ensembling convolutional neural network and statistical features | 1810.12832 | http://arxiv.org/abs/1810.12832v1 | http://arxiv.org/pdf/1810.12832v1.pdf | https://github.com/r0mer0m/learning_audio_modeling | false | false | true | pytorch |
https://paperswithcode.com/paper/learning-to-benchmark-determining-best | Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data | 1909.07192 | https://arxiv.org/abs/1909.07192v1 | https://arxiv.org/pdf/1909.07192v1.pdf | https://github.com/mrtnoshad/Bayes_Error_Estimator | false | false | true | none |
https://paperswithcode.com/paper/bert-has-a-mouth-and-it-must-speak-bert-as-a | BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model | 1902.04094 | http://arxiv.org/abs/1902.04094v2 | http://arxiv.org/pdf/1902.04094v2.pdf | https://github.com/vatsal199/Obedient_BERT | false | false | true | none |
https://paperswithcode.com/paper/a-unified-successive-pseudo-convex | A Unified Successive Pseudo-Convex Approximation Framework | 1506.04972 | https://arxiv.org/abs/1506.04972v2 | https://arxiv.org/pdf/1506.04972v2.pdf | https://github.com/optyang/STELA | false | false | true | none |
https://paperswithcode.com/paper/cutmix-regularization-strategy-to-train | CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features | 1905.04899 | https://arxiv.org/abs/1905.04899v2 | https://arxiv.org/pdf/1905.04899v2.pdf | https://github.com/Kaushal28/CutMix-Regularization-using-PyTorch | false | false | true | pytorch |
https://paperswithcode.com/paper/on-the-texture-bias-for-few-shot-cnn | On the Texture Bias for Few-Shot CNN Segmentation | 2003.04052 | https://arxiv.org/abs/2003.04052v3 | https://arxiv.org/pdf/2003.04052v3.pdf | https://github.com/rezazad68/fewshot-segmentation | true | true | true | tf |
https://paperswithcode.com/paper/kermit-complementing-transformer | KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations | null | https://aclanthology.org/2020.emnlp-main.18 | https://aclanthology.org/2020.emnlp-main.18.pdf | https://github.com/ART-Group-it/KERMIT | true | false | false | none |
https://paperswithcode.com/paper/solving-even-parity-problems-using-traceless | Solving even-parity problems using traceless genetic programming | 2110.02014 | https://arxiv.org/abs/2110.02014v1 | https://arxiv.org/pdf/2110.02014v1.pdf | https://github.com/mihaioltean/traceless-genetic-programming | true | true | false | none |
https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | 1602.07261 | http://arxiv.org/abs/1602.07261v2 | http://arxiv.org/pdf/1602.07261v2.pdf | https://github.com/waynecoffee9/Traffic-Sign-Classifier | false | false | true | tf |
https://paperswithcode.com/paper/the-winnability-of-klondike-and-many-other | The Winnability of Klondike Solitaire and Many Other Patience Games | 1906.12314 | https://arxiv.org/abs/1906.12314v5 | https://arxiv.org/pdf/1906.12314v5.pdf | https://github.com/thecharlieblake/Solvitaire | true | false | false | none |
https://paperswithcode.com/paper/dynaslam-tracking-mapping-and-inpainting-in | DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes | 1806.05620 | http://arxiv.org/abs/1806.05620v2 | http://arxiv.org/pdf/1806.05620v2.pdf | https://github.com/linmeeka/slamProject | false | false | true | tf |
https://paperswithcode.com/paper/multinet-real-time-joint-semantic-reasoning | MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving | 1612.07695 | http://arxiv.org/abs/1612.07695v2 | http://arxiv.org/pdf/1612.07695v2.pdf | https://github.com/ziyuan400/video_segmentation | false | false | true | tf |
https://paperswithcode.com/paper/squeeze-and-excitation-networks | Squeeze-and-Excitation Networks | 1709.01507 | https://arxiv.org/abs/1709.01507v4 | https://arxiv.org/pdf/1709.01507v4.pdf | https://github.com/tsubasawb/DeepLearning_Paper | false | false | true | none |
https://paperswithcode.com/paper/mobilenets-efficient-convolutional-neural | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | 1704.04861 | http://arxiv.org/abs/1704.04861v1 | http://arxiv.org/pdf/1704.04861v1.pdf | https://github.com/tsubasawb/DeepLearning_Paper | false | false | true | none |
https://paperswithcode.com/paper/policyspace-a-modeling-platform | PolicySpace: a modeling platform | 1801.00259 | http://arxiv.org/abs/1801.00259v1 | http://arxiv.org/pdf/1801.00259v1.pdf | https://github.com/IpeaDISET/PolicySpace | false | false | true | none |
https://paperswithcode.com/paper/self-critical-sequence-training-for-image | Self-critical Sequence Training for Image Captioning | 1612.00563 | http://arxiv.org/abs/1612.00563v2 | http://arxiv.org/pdf/1612.00563v2.pdf | https://github.com/xiaobai714/image_caption | false | false | true | pytorch |
https://paperswithcode.com/paper/flexible-marginal-models-for-dependent-data | Flexible Marginal Models for Dependent Data | 2204.07188 | https://arxiv.org/abs/2204.07188v1 | https://arxiv.org/pdf/2204.07188v1.pdf | https://github.com/awstringer1/mam | true | true | false | none |
https://paperswithcode.com/paper/unsupervised-representation-learning-with-1 | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | 1511.06434 | http://arxiv.org/abs/1511.06434v2 | http://arxiv.org/pdf/1511.06434v2.pdf | https://github.com/gagan16/DcGan-Tensorflow | false | false | true | tf |
https://paperswithcode.com/paper/you-only-look-once-unified-real-time-object | You Only Look Once: Unified, Real-Time Object Detection | 1506.02640 | http://arxiv.org/abs/1506.02640v5 | http://arxiv.org/pdf/1506.02640v5.pdf | https://github.com/leon-liangwu/py-caffe-yolo | false | false | true | caffe2 |
https://paperswithcode.com/paper/ask-me-anything-dynamic-memory-networks-for | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing | 1506.07285 | http://arxiv.org/abs/1506.07285v5 | http://arxiv.org/pdf/1506.07285v5.pdf | https://github.com/scakc/QAwiki | false | false | true | none |
https://paperswithcode.com/paper/video-to-video-synthesis | Video-to-Video Synthesis | 1808.06601 | http://arxiv.org/abs/1808.06601v2 | http://arxiv.org/pdf/1808.06601v2.pdf | https://github.com/divyanshpuri02/divyansh.github.io | false | false | true | pytorch |
https://paperswithcode.com/paper/diversity-in-valuing-social-contact-and-risk | Diversity in Valuing Social Contact and Risk Tolerance Lead to the Emergence of Homophily in Populations Facing Infectious Threats | 2111.11362 | https://arxiv.org/abs/2111.11362v1 | https://arxiv.org/pdf/2111.11362v1.pdf | https://github.com/kazarraha/socdistmodel | true | true | false | none |
https://paperswithcode.com/paper/bag-of-tricks-for-efficient-text | Bag of Tricks for Efficient Text Classification | 1607.01759 | http://arxiv.org/abs/1607.01759v3 | http://arxiv.org/pdf/1607.01759v3.pdf | https://github.com/FengJiaChunFromSYSU/fastText | false | false | true | none |
https://paperswithcode.com/paper/fcos-fully-convolutional-one-stage-object | FCOS: Fully Convolutional One-Stage Object Detection | 1904.01355 | https://arxiv.org/abs/1904.01355v5 | https://arxiv.org/pdf/1904.01355v5.pdf | https://github.com/abcxs/maskrcnn-contest | false | false | true | pytorch |
https://paperswithcode.com/paper/retinamask-learning-to-predict-masks-improves | RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free | 1901.03353 | http://arxiv.org/abs/1901.03353v1 | http://arxiv.org/pdf/1901.03353v1.pdf | https://github.com/abcxs/maskrcnn-contest | false | false | true | pytorch |
https://paperswithcode.com/paper/how-to-make-chord-correct | How to Make Chord Correct | 1502.06461 | http://arxiv.org/abs/1502.06461v2 | http://arxiv.org/pdf/1502.06461v2.pdf | https://github.com/kratikagupta-developer/CHORD-Protocol-Implementation | false | false | true | none |
https://paperswithcode.com/paper/cross-domain-ensemble-distillation-for-domain-2 | Cross-Domain Ensemble Distillation for Domain Generalization | 2211.14058 | https://arxiv.org/abs/2211.14058v1 | https://arxiv.org/pdf/2211.14058v1.pdf | https://github.com/leekyungmoon/XDED | true | true | false | pytorch |
https://paperswithcode.com/paper/190600722 | Topological Autoencoders | 1906.00722 | https://arxiv.org/abs/1906.00722v5 | https://arxiv.org/pdf/1906.00722v5.pdf | https://github.com/BorgwardtLab/topo-ae-distances | false | false | true | pytorch |
https://paperswithcode.com/paper/qanet-combining-local-convolution-with-global | QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension | 1804.09541 | http://arxiv.org/abs/1804.09541v1 | http://arxiv.org/pdf/1804.09541v1.pdf | https://github.com/shikhar1sharma/NLP-Resources | false | false | true | none |
https://paperswithcode.com/paper/on-the-fly-aligned-data-augmentation-for | On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR | 2104.01393 | https://arxiv.org/abs/2104.01393v2 | https://arxiv.org/pdf/2104.01393v2.pdf | https://github.com/StatNLP/ada4asr | true | true | false | pytorch |
https://paperswithcode.com/paper/towards-the-automatic-anime-characters | Towards the Automatic Anime Characters Creation with Generative Adversarial Networks | 1708.05509 | http://arxiv.org/abs/1708.05509v1 | http://arxiv.org/pdf/1708.05509v1.pdf | https://github.com/MasayaGit/AnimeGAN | false | false | true | pytorch |
https://paperswithcode.com/paper/video-captioning-with-recurrent-networks | Video captioning with recurrent networks based on frame- and video-level features and visual content classification | 1512.02949 | http://arxiv.org/abs/1512.02949v1 | http://arxiv.org/pdf/1512.02949v1.pdf | https://github.com/rakshithShetty/captionGAN | false | false | true | none |
https://paperswithcode.com/paper/fasttrack-an-open-source-software-for | FastTrack: an open-source software for tracking varying numbers of deformable objects | 2011.06837 | https://arxiv.org/abs/2011.06837v1 | https://arxiv.org/pdf/2011.06837v1.pdf | https://github.com/FastTrackOrg/FastTrack | true | true | false | none |
https://paperswithcode.com/paper/learning-semantically-enhanced-feature-for | Learning Semantically Enhanced Feature for Fine-Grained Image Classification | 2006.13457 | https://arxiv.org/abs/2006.13457v3 | https://arxiv.org/pdf/2006.13457v3.pdf | https://github.com/YNCao/mysef | false | false | true | pytorch |
https://paperswithcode.com/paper/online-abuse-detection-the-value-of | Online abuse detection: the value of preprocessing and neural attention models | null | https://aclanthology.org/W19-1303 | https://aclanthology.org/W19-1303.pdf | https://github.com/ddhruvkr/Online_Abuse_Detection | true | true | false | pytorch |
https://paperswithcode.com/paper/debface-de-biasing-face-recognition | Jointly De-biasing Face Recognition and Demographic Attribute Estimation | 1911.08080 | https://arxiv.org/abs/1911.08080v4 | https://arxiv.org/pdf/1911.08080v4.pdf | https://github.com/gongsixue/DebFace | true | true | false | pytorch |
https://paperswithcode.com/paper/explaining-anomalies-detected-by-autoencoders | Explaining Anomalies Detected by Autoencoders Using SHAP | 1903.02407 | https://arxiv.org/abs/1903.02407v2 | https://arxiv.org/pdf/1903.02407v2.pdf | https://github.com/ronniemi/explainAnomaliesUsingSHAP | true | true | false | tf |
https://paperswithcode.com/paper/painting-with-baryons-augmenting-n-body | Painting with baryons: augmenting N-body simulations with gas using deep generative models | 1903.12173 | https://arxiv.org/abs/1903.12173v2 | https://arxiv.org/pdf/1903.12173v2.pdf | https://github.com/tilmantroester/baryon_painter | true | true | true | none |
https://paperswithcode.com/paper/teaching-temporal-logics-to-neural-networks | Teaching Temporal Logics to Neural Networks | 2003.04218 | https://arxiv.org/abs/2003.04218v3 | https://arxiv.org/pdf/2003.04218v3.pdf | https://github.com/reactive-systems/deepltl | true | true | true | tf |
https://paperswithcode.com/paper/smart-mc-sparse-matrix-estimation-with | SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model | 2412.03596 | https://arxiv.org/abs/2412.03596v2 | https://arxiv.org/pdf/2412.03596v2.pdf | https://github.com/priyamdas2/SMART-MC-MSCOR | true | false | false | none |
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector | SSD: Single Shot MultiBox Detector | 1512.02325 | http://arxiv.org/abs/1512.02325v5 | http://arxiv.org/pdf/1512.02325v5.pdf | https://github.com/victordibia/handtracking | false | false | true | tf |
https://paperswithcode.com/paper/corresponding-projections-for-orphan | Corresponding Projections for Orphan Screening | 1812.00058 | http://arxiv.org/abs/1812.00058v1 | http://arxiv.org/pdf/1812.00058v1.pdf | https://github.com/diogofbraga/OrphanPrincipalComponentAnalysis | false | false | true | none |
https://paperswithcode.com/paper/robustness-quantification-for-classification | Adversarial Robustness Guarantees for Classification with Gaussian Processes | 1905.11876 | https://arxiv.org/abs/1905.11876v3 | https://arxiv.org/pdf/1905.11876v3.pdf | https://github.com/andreapatane/check-GPclass | true | true | true | none |
https://paperswithcode.com/paper/real-time-and-accurate-object-detection-in | Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation | 2103.14529 | https://arxiv.org/abs/2103.14529v1 | https://arxiv.org/pdf/2103.14529v1.pdf | https://github.com/hustvl/LSFA | true | true | false | mxnet |
https://paperswithcode.com/paper/on-catastrophic-interference-in-atari-2600 | On Catastrophic Interference in Atari 2600 Games | 2002.12499 | https://arxiv.org/abs/2002.12499v2 | https://arxiv.org/pdf/2002.12499v2.pdf | https://github.com/google-research/google-research/tree/master/memento | true | false | false | tf |
https://paperswithcode.com/paper/topological-control-of-synchronization | Topological Control of Synchronization Patterns: Trading Symmetry for Stability | 1902.03255 | https://arxiv.org/abs/1902.03255v1 | https://arxiv.org/pdf/1902.03255v1.pdf | https://github.com/y-z-zhang/optimize_sym_cluster | true | true | false | none |
https://paperswithcode.com/paper/knowledge-tracing-for-complex-problem-solving | Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization | 2210.09013 | https://arxiv.org/abs/2210.09013v1 | https://arxiv.org/pdf/2210.09013v1.pdf | https://github.com/persai-lab/umap2021-grate | true | true | false | none |
https://paperswithcode.com/paper/optimization-of-molecules-via-deep | Optimization of Molecules via Deep Reinforcement Learning | 1810.08678 | http://arxiv.org/abs/1810.08678v3 | http://arxiv.org/pdf/1810.08678v3.pdf | https://github.com/google-research/google-research/tree/master/mol_dqn | true | false | false | tf |
https://paperswithcode.com/paper/drop-an-octave-reducing-spatial-redundancy-in | Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution | 1904.05049 | https://arxiv.org/abs/1904.05049v3 | https://arxiv.org/pdf/1904.05049v3.pdf | https://github.com/SharadGitHub/OctaveUnet | false | false | true | pytorch |
https://paperswithcode.com/paper/digging-into-self-supervised-monocular-depth | Digging Into Self-Supervised Monocular Depth Estimation | 1806.01260 | https://arxiv.org/abs/1806.01260v4 | https://arxiv.org/pdf/1806.01260v4.pdf | https://github.com/FangGet/tf-monodepth2 | false | false | true | tf |
https://paperswithcode.com/paper/a-fully-differentiable-beam-search-decoder | A Fully Differentiable Beam Search Decoder | 1902.06022 | http://arxiv.org/abs/1902.06022v1 | http://arxiv.org/pdf/1902.06022v1.pdf | https://github.com/johnhw/differentiable_sorting | false | false | true | tf |
https://paperswithcode.com/paper/statistical-models-for-the-analysis-of | Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions | 2010.03783 | https://arxiv.org/abs/2010.03783v4 | https://arxiv.org/pdf/2010.03783v4.pdf | https://github.com/davidissamattos/statscomp | true | false | false | none |
https://paperswithcode.com/paper/automatically-designing-cnn-architectures | Automatically designing CNN architectures using genetic algorithm for image classification | 1808.03818 | https://arxiv.org/abs/1808.03818v3 | https://arxiv.org/pdf/1808.03818v3.pdf | https://github.com/Marius-Juston/AutoCNN | false | false | true | tf |
https://paperswithcode.com/paper/an-application-of-paraexp-to-electromagnetic | An Application of ParaExp to Electromagnetic Wave Problems | 1607.00368 | https://arxiv.org/abs/1607.00368v2 | https://arxiv.org/pdf/1607.00368v2.pdf | https://github.com/temf/paraexp | true | false | false | none |
https://paperswithcode.com/paper/paraexp-using-leapfrog-as-integrator-for-high | ParaExp using Leapfrog as Integrator for High-Frequency Electromagnetic Simulations | 1705.08019 | https://arxiv.org/abs/1705.08019v2 | https://arxiv.org/pdf/1705.08019v2.pdf | https://github.com/temf/paraexp | true | false | false | none |
https://paperswithcode.com/paper/asymmetric-statistical-errors | Asymmetric Statistical Errors | physics/0406120 | https://arxiv.org/abs/physics/0406120v1 | https://arxiv.org/pdf/physics/0406120v1.pdf | https://github.com/muryelgp/asymmetric_uncertainties | false | false | true | none |
https://paperswithcode.com/paper/are-undocumented-workers-the-same-as-illegal | Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces | 2010.02976 | https://arxiv.org/abs/2010.02976v2 | https://arxiv.org/pdf/2010.02976v2.pdf | https://github.com/awebson/congressional_adversary | true | true | true | pytorch |
https://paperswithcode.com/paper/sample-efficient-actor-critic-with-experience | Sample Efficient Actor-Critic with Experience Replay | 1611.01224 | http://arxiv.org/abs/1611.01224v2 | http://arxiv.org/pdf/1611.01224v2.pdf | https://github.com/Kaixhin/ACER | false | false | true | pytorch |
https://paperswithcode.com/paper/fedhca2-towards-hetero-client-federated-multi | FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | null | http://openaccess.thecvf.com//content/CVPR2024/html/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html | http://openaccess.thecvf.com//content/CVPR2024/papers/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.pdf | https://github.com/innovator-zero/fedhca2 | true | true | false | pytorch |
https://paperswithcode.com/paper/naturalization-of-text-by-the-insertion-of | Naturalization of Text by the Insertion of Pauses and Filler Words | 2011.03713 | https://arxiv.org/abs/2011.03713v1 | https://arxiv.org/pdf/2011.03713v1.pdf | https://github.com/parthvshah/naturalization | true | false | false | none |
https://paperswithcode.com/paper/probabilistic-event-calculus-for-event | Probabilistic Event Calculus for Event Recognition | 1207.3270 | http://arxiv.org/abs/1207.3270v2 | http://arxiv.org/pdf/1207.3270v2.pdf | https://github.com/koo5/notes2 | false | false | true | none |
https://paperswithcode.com/paper/mean-subtraction-and-mode-selection-in | Clarifying the effect of mean subtraction on Dynamic Mode Decomposition | 2105.03607 | https://arxiv.org/abs/2105.03607v6 | https://arxiv.org/pdf/2105.03607v6.pdf | https://github.com/gowtham-ss-ragavan/msub_mdselect_dmd | true | true | false | none |
https://paperswithcode.com/paper/deeperforensics-10-a-large-scale-dataset-for | DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection | 2001.03024 | https://arxiv.org/abs/2001.03024v2 | https://arxiv.org/pdf/2001.03024v2.pdf | https://github.com/EndlessSora/DeeperForensics-1.0 | true | false | true | none |
https://paperswithcode.com/paper/3d-object-reconstruction-from-hand-object | 3D Object Reconstruction from Hand-Object Interactions | 1704.00529 | http://arxiv.org/abs/1704.00529v1 | http://arxiv.org/pdf/1704.00529v1.pdf | https://github.com/dimtziwnas/InHandScanningICCV15_Reconstruction | true | false | false | none |
https://paperswithcode.com/paper/simple-and-effective-vae-training-with | Simple and Effective VAE Training with Calibrated Decoders | 2006.13202 | https://arxiv.org/abs/2006.13202v3 | https://arxiv.org/pdf/2006.13202v3.pdf | https://github.com/orybkin/sigma-vae | true | false | false | tf |
https://paperswithcode.com/paper/multi-agent-generative-adversarial-imitation | Multi-Agent Generative Adversarial Imitation Learning | 1807.09936 | http://arxiv.org/abs/1807.09936v1 | http://arxiv.org/pdf/1807.09936v1.pdf | https://github.com/ermongroup/multiagent-gail | false | false | true | none |
https://paperswithcode.com/paper/where-s-crypto-automated-identification-and | Where's Crypto?: Automated Identification and Classification of Proprietary Cryptographic Primitives in Binary Code | 2009.04274 | https://arxiv.org/abs/2009.04274v1 | https://arxiv.org/pdf/2009.04274v1.pdf | https://github.com/wheres-crypto/wheres-crypto | true | true | false | none |
https://paperswithcode.com/paper/scalable-learning-of-non-decomposable | Scalable Learning of Non-Decomposable Objectives | 1608.04802 | http://arxiv.org/abs/1608.04802v2 | http://arxiv.org/pdf/1608.04802v2.pdf | https://github.com/tensorflow/models/tree/master/research/global_objectives | false | false | true | tf |
https://paperswithcode.com/paper/you-only-derive-once-yodo-automatic | You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks | 2206.08687 | https://arxiv.org/abs/2206.08687v1 | https://arxiv.org/pdf/2206.08687v1.pdf | https://github.com/rballester/yodo | true | true | true | pytorch |
https://paperswithcode.com/paper/mri-to-ct-translation-with-gans | MRI to CT Translation with GANs | 1901.05259 | http://arxiv.org/abs/1901.05259v1 | http://arxiv.org/pdf/1901.05259v1.pdf | https://github.com/bodokaiser/mrtoct-pytorch | true | false | false | pytorch |
https://paperswithcode.com/paper/music-genre-classification-using-machine | Music Genre Classification using Machine Learning Techniques | 1804.01149 | http://arxiv.org/abs/1804.01149v1 | http://arxiv.org/pdf/1804.01149v1.pdf | https://github.com/HareeshBahuleyan/music-genre-classification | true | true | true | tf |
https://paperswithcode.com/paper/rainfall-runoff-prediction-at-multiple | Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network | 2010.07921 | https://arxiv.org/abs/2010.07921v1 | https://arxiv.org/pdf/2010.07921v1.pdf | https://github.com/gauchm/mts-lstm | true | true | true | pytorch |
https://paperswithcode.com/paper/empty-cities-a-dynamic-object-invariant-space | Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM | 2010.07646 | https://arxiv.org/abs/2010.07646v1 | https://arxiv.org/pdf/2010.07646v1.pdf | https://github.com/bertabescos/EmptyCities_SLAM | true | true | false | pytorch |
https://paperswithcode.com/paper/new-evolutionary-computation-models-and-their | New Evolutionary Computation Models and their Applications to Machine Learning | 2110.00468 | https://arxiv.org/abs/2110.00468v1 | https://arxiv.org/pdf/2110.00468v1.pdf | https://github.com/mihaioltean/traceless-genetic-programming | true | false | false | none |
https://paperswithcode.com/paper/vehicle-predictive-trajectory-patterns-from | Vehicle predictive trajectory patterns from isochronous data | 2010.05026 | https://arxiv.org/abs/2010.05026v2 | https://arxiv.org/pdf/2010.05026v2.pdf | https://github.com/Seeker3000/AUD | true | false | false | none |
https://paperswithcode.com/paper/a-run-and-tumble-model-with-autochemotaxis | A Run-and-Tumble Model with Autochemotaxis | 2009.03221 | https://arxiv.org/abs/2009.03221v1 | https://arxiv.org/pdf/2009.03221v1.pdf | https://github.com/Louminator/Plankton_Signal_RT | true | true | false | none |
https://paperswithcode.com/paper/zinet-linking-chinese-characters-spanning | ZiNet: Linking Chinese Characters Spanning Three Thousand Years | null | https://aclanthology.org/2022.findings-acl.242 | https://aclanthology.org/2022.findings-acl.242.pdf | https://github.com/yangchijlu/ancientchinesecharsim | true | true | false | pytorch |
https://paperswithcode.com/paper/low-dose-ct-image-denoising-using-a | Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss | 1708.00961 | http://arxiv.org/abs/1708.00961v2 | http://arxiv.org/pdf/1708.00961v2.pdf | https://github.com/SSinyu/WGAN-VGG | false | false | true | pytorch |
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 | true | true | true | tf |
https://paperswithcode.com/paper/human-and-automatic-detection-of-generated | Automatic Detection of Generated Text is Easiest when Humans are Fooled | 1911.00650 | https://arxiv.org/abs/1911.00650v2 | https://arxiv.org/pdf/1911.00650v2.pdf | https://github.com/kirubarajan/roft | false | false | true | none |
https://paperswithcode.com/paper/correlation-aware-deep-generative-model-for | Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection | 2002.07349 | https://arxiv.org/abs/2002.07349v3 | https://arxiv.org/pdf/2002.07349v3.pdf | https://github.com/haoyfan/CADGMM | true | false | true | tf |
https://paperswithcode.com/paper/towards-ai-complete-question-answering-a-set | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | 1502.05698 | http://arxiv.org/abs/1502.05698v10 | http://arxiv.org/pdf/1502.05698v10.pdf | https://github.com/kirubarajan/roft | false | false | true | none |
https://paperswithcode.com/paper/verification-of-hierarchical-artifact-systems | Verification of Hierarchical Artifact Systems | 1604.00967 | http://arxiv.org/abs/1604.00967v1 | http://arxiv.org/pdf/1604.00967v1.pdf | https://github.com/oi02lyl/has-verifier | false | false | true | none |
https://paperswithcode.com/paper/discontinuous-transition-of-molecular | Discontinuous transition of molecular-hydrogen chain to the quasi-atomic state: Exact diagonalization - ab initio approach | 1506.03356 | https://arxiv.org/abs/1506.03356v2 | https://arxiv.org/pdf/1506.03356v2.pdf | https://bitbucket.org/azja/qmt | true | true | false | none |
https://paperswithcode.com/paper/metallization-of-solid-molecular-hydrogen-in | Metallization of solid molecular hydrogen in two dimensions: Mott-Hubbard-type transition | 1702.06575 | https://arxiv.org/abs/1702.06575v1 | https://arxiv.org/pdf/1702.06575v1.pdf | https://bitbucket.org/azja/qmt | true | false | false | none |
https://paperswithcode.com/paper/combined-shared-and-distributed-memory-ab | Combined shared and distributed memory ab-initio computations of molecular-hydrogen systems in the correlated state: process pool solution and two-level parallelism | 1504.00500 | https://arxiv.org/abs/1504.00500v3 | https://arxiv.org/pdf/1504.00500v3.pdf | https://bitbucket.org/azja/qmt | true | true | false | none |
https://paperswithcode.com/paper/dot-ring-nanostructure-rigorous-analysis-of | Dot-ring nanostructure: Rigorous analysis of many-electron effects | 1605.01195 | https://arxiv.org/abs/1605.01195v1 | https://arxiv.org/pdf/1605.01195v1.pdf | https://bitbucket.org/azja/qmt | true | true | false | none |
https://paperswithcode.com/paper/automatic-design-of-mechanical-metamaterial | Automatic Design of Mechanical Metamaterial Actuators | 2002.03032 | https://arxiv.org/abs/2002.03032v1 | https://arxiv.org/pdf/2002.03032v1.pdf | https://github.com/ComplexityBiosystems/metamech_datasets | false | false | true | none |
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