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/review-highlights-opinion-mining-on-reviews-a | Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction | null | https://dl.acm.org/citation.cfm?id=3158385 | http://vixra.org/pdf/1910.0514v1.pdf | https://github.com/yardstick17/AspectBasedSentimentAnalysis | false | false | false | none |
https://paperswithcode.com/paper/temporally-coherent-gans-for-video-super | Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation | 1811.09393 | https://arxiv.org/abs/1811.09393v4 | https://arxiv.org/pdf/1811.09393v4.pdf | https://github.com/GitHubXlong/TecoGAN | false | false | true | pytorch |
https://paperswithcode.com/paper/ckmeans-and-fckmeans-two-deterministic | CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance | 2304.09989 | https://arxiv.org/abs/2304.09989v2 | https://arxiv.org/pdf/2304.09989v2.pdf | https://github.com/Layebuniv/fckmeans | true | false | false | none |
https://paperswithcode.com/paper/1-ogc-the-first-open-gravitational-wave | 1-OGC: The first open gravitational-wave catalog of binary mergers from analysis of public Advanced LIGO data | 1811.01921 | http://arxiv.org/abs/1811.01921v2 | http://arxiv.org/pdf/1811.01921v2.pdf | https://github.com/gwastro/1-ogc | true | true | true | none |
https://paperswithcode.com/paper/modeling-the-dynamics-of-online-learning | Modeling the Dynamics of Online Learning Activity | 1610.05775 | http://arxiv.org/abs/1610.05775v1 | http://arxiv.org/pdf/1610.05775v1.pdf | https://github.com/Networks-Learning/hdhp.py | true | true | true | none |
https://paperswithcode.com/paper/hierarchical-density-order-embeddings | Hierarchical Density Order Embeddings | 1804.09843 | http://arxiv.org/abs/1804.09843v1 | http://arxiv.org/pdf/1804.09843v1.pdf | https://github.com/benathi/density-order-emb | true | true | false | pytorch |
https://paperswithcode.com/paper/kernalised-multi-resolution-convnet-for | Kernalised Multi-resolution Convnet for Visual Tracking | 1708.00577 | http://arxiv.org/abs/1708.00577v1 | http://arxiv.org/pdf/1708.00577v1.pdf | https://github.com/stevenwudi/KMC_cvprw_2017 | true | true | false | tf |
https://paperswithcode.com/paper/crowdsourcing-lightweight-pyramids-for-manual | Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation | 1904.05929 | http://arxiv.org/abs/1904.05929v1 | http://arxiv.org/pdf/1904.05929v1.pdf | https://github.com/OriShapira/LitePyramids | true | true | false | none |
https://paperswithcode.com/paper/spatiotemporal-residual-networks-for-video | Spatiotemporal Residual Networks for Video Action Recognition | 1611.02155 | http://arxiv.org/abs/1611.02155v1 | http://arxiv.org/pdf/1611.02155v1.pdf | https://github.com/feichtenhofer/st-resnet | true | true | false | none |
https://paperswithcode.com/paper/generative-partition-networks-for-multi | Generative Partition Networks for Multi-Person Pose Estimation | 1705.07422 | http://arxiv.org/abs/1705.07422v2 | http://arxiv.org/pdf/1705.07422v2.pdf | https://github.com/NieXC/pytorch-ppn | false | false | true | pytorch |
https://paperswithcode.com/paper/traffic-graph-convolutional-recurrent-neural | Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting | 1802.07007 | https://arxiv.org/abs/1802.07007v3 | https://arxiv.org/pdf/1802.07007v3.pdf | https://github.com/zhiyongc/Seattle-Loop-Data | true | true | false | none |
https://paperswithcode.com/paper/surfacenet-an-end-to-end-3d-neural-network | SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis | 1708.01749 | http://arxiv.org/abs/1708.01749v1 | http://arxiv.org/pdf/1708.01749v1.pdf | https://github.com/mjiUST/SurfaceNet | true | true | false | none |
https://paperswithcode.com/paper/geometric-adaptive-monte-carlo-in-random | Geometric adaptive Monte Carlo in random environment | 1608.07986 | https://arxiv.org/abs/1608.07986v4 | https://arxiv.org/pdf/1608.07986v4.pdf | https://github.com/scidom/MAMALASampler.jl | true | true | false | none |
https://paperswithcode.com/paper/random-directions-stochastic-approximation | Random directions stochastic approximation with deterministic perturbations | 1808.02871 | http://arxiv.org/abs/1808.02871v2 | http://arxiv.org/pdf/1808.02871v2.pdf | https://github.com/prashla/RDSA | true | true | false | none |
https://paperswithcode.com/paper/manifoldnet-a-deep-network-framework-for | ManifoldNet: A Deep Network Framework for Manifold-valued Data | 1809.06211 | http://arxiv.org/abs/1809.06211v3 | http://arxiv.org/pdf/1809.06211v3.pdf | https://github.com/jjbouza/manifold-net | true | true | false | pytorch |
https://paperswithcode.com/paper/show-and-tell-a-neural-image-caption | Show and Tell: A Neural Image Caption Generator | 1411.4555 | http://arxiv.org/abs/1411.4555v2 | http://arxiv.org/pdf/1411.4555v2.pdf | https://github.com/kirbiyik/caption-it | false | false | true | pytorch |
https://paperswithcode.com/paper/disentangling-factors-of-variation-with-cycle | Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders | 1804.10469 | http://arxiv.org/abs/1804.10469v1 | http://arxiv.org/pdf/1804.10469v1.pdf | https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training | false | false | true | pytorch |
https://paperswithcode.com/paper/disentangling-factors-of-variation-in-deep | Disentangling factors of variation in deep representations using adversarial training | 1611.03383 | http://arxiv.org/abs/1611.03383v1 | http://arxiv.org/pdf/1611.03383v1.pdf | https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training | false | false | true | pytorch |
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical | U-Net: Convolutional Networks for Biomedical Image Segmentation | 1505.04597 | http://arxiv.org/abs/1505.04597v1 | http://arxiv.org/pdf/1505.04597v1.pdf | https://github.com/neshitov/UNet | false | false | true | pytorch |
https://paperswithcode.com/paper/audino-a-modern-annotation-tool-for-audio-and | audino: A Modern Annotation Tool for Audio and Speech | 2006.05236 | https://arxiv.org/abs/2006.05236v2 | https://arxiv.org/pdf/2006.05236v2.pdf | https://github.com/midas-research/audino | true | true | true | none |
https://paperswithcode.com/paper/robust-adversarial-reinforcement-learning | Robust Adversarial Reinforcement Learning | 1703.02702 | http://arxiv.org/abs/1703.02702v1 | http://arxiv.org/pdf/1703.02702v1.pdf | https://github.com/davidsonic/robust-grasp | false | false | true | tf |
https://paperswithcode.com/paper/chainercv-a-library-for-deep-learning-in | ChainerCV: a Library for Deep Learning in Computer Vision | 1708.08169 | http://arxiv.org/abs/1708.08169v1 | http://arxiv.org/pdf/1708.08169v1.pdf | https://github.com/chainer/chainercv | false | false | true | none |
https://paperswithcode.com/paper/denoising-diffusion-probabilistic-models | Denoising Diffusion Probabilistic Models | 2006.11239 | https://arxiv.org/abs/2006.11239v2 | https://arxiv.org/pdf/2006.11239v2.pdf | https://github.com/sak-h/pytorch-Denoising-Diffusion-Probabilistic-Models | false | false | true | pytorch |
https://paperswithcode.com/paper/vo-tranh-eternal-pulse-o-lumina-genesis | Vô Tranh Eternal Pulse Ω – Lumina Genesis | null | https://zenodo.org/records/15132859 | https://zenodo.org/records/15132859/files/WHITEPAPER.pdf | https://github.com/vinhatson/The-Last---Lumina-genesis | false | false | false | pytorch |
https://paperswithcode.com/paper/variational-dropout-sparsifies-deep-neural | Variational Dropout Sparsifies Deep Neural Networks | 1701.05369 | http://arxiv.org/abs/1701.05369v3 | http://arxiv.org/pdf/1701.05369v3.pdf | https://github.com/ars-ashuha/sparse-vd-pytorch | false | false | true | pytorch |
https://paperswithcode.com/paper/learning-from-simulated-and-unsupervised | Learning from Simulated and Unsupervised Images through Adversarial Training | 1612.07828 | http://arxiv.org/abs/1612.07828v2 | http://arxiv.org/pdf/1612.07828v2.pdf | https://github.com/rickyhan/SimGAN-Captcha | false | false | true | tf |
https://paperswithcode.com/paper/savoias-a-diverse-multi-category-visual | SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset | 1810.01771 | http://arxiv.org/abs/1810.01771v1 | http://arxiv.org/pdf/1810.01771v1.pdf | https://github.com/esaraee/Savoias-Dataset | true | true | false | none |
https://paperswithcode.com/paper/depth-map-prediction-from-a-single-image | Depth Map Prediction from a Single Image using a Multi-Scale Deep Network | 1406.2283 | http://arxiv.org/abs/1406.2283v1 | http://arxiv.org/pdf/1406.2283v1.pdf | https://github.com/MasazI/cnn_depth_tensorflow | false | false | true | tf |
https://paperswithcode.com/paper/word-embeddings-for-the-analysis-of | Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora | null | https://www.cambridge.org/core/journals/political-analysis/article/abs/word-embeddings-for-the-analysis-of-ideological-placement-in-parliamentary-corpora/017F0CEA9B3DB6E1B94AC36A509A8A7B | https://ludovicrheault.weebly.com/uploads/3/9/4/0/39408253/rheaultcochrane2019_pa.pdf | https://github.com/lrheault/partyembed | true | true | false | none |
https://paperswithcode.com/paper/how-emotional-are-you-neural-architectures | How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs | null | https://aclanthology.org/C18-1247 | https://aclanthology.org/C18-1247.pdf | https://github.com/Pranav-Goel/Neural_Emotion_Intensity_Prediction | true | true | false | tf |
https://paperswithcode.com/paper/afet-automatic-fine-grained-entity-typing-by | AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding | null | https://aclanthology.org/D16-1144 | https://aclanthology.org/D16-1144.pdf | https://github.com/shanzhenren/AFET | true | true | false | none |
https://paperswithcode.com/paper/the-signature-of-large-scale-turbulence | The signature of large scale turbulence driving on the structure of the interstellar medium | 2206.00451 | https://arxiv.org/abs/2206.00451v1 | https://arxiv.org/pdf/2206.00451v1.pdf | https://bitbucket.org/rteyssie/ramses | true | false | false | none |
https://paperswithcode.com/paper/the-fermilab-muon-g-2-straw-tracking | The Fermilab Muon $g-2$ straw tracking detectors, internal tracker alignment, and the muon EDM measurement | 1909.12900 | https://arxiv.org/abs/1909.12900v2 | https://arxiv.org/pdf/1909.12900v2.pdf | https://github.com/glukicov/alignTrack | false | false | true | none |
https://paperswithcode.com/paper/two-local-models-for-neural-constituent | Two Local Models for Neural Constituent Parsing | 1808.04850 | http://arxiv.org/abs/1808.04850v2 | http://arxiv.org/pdf/1808.04850v2.pdf | https://github.com/zeeeyang/two-local-neural-conparsers | true | true | false | none |
https://paperswithcode.com/paper/learning-deep-features-for-discriminative | Learning Deep Features for Discriminative Localization | 1512.04150 | http://arxiv.org/abs/1512.04150v1 | http://arxiv.org/pdf/1512.04150v1.pdf | https://github.com/tensorpack/tensorpack/tree/master/examples/Saliency | false | false | false | tf |
https://paperswithcode.com/paper/a-fast-and-scalable-joint-estimator-for-1 | A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models | 1806.00548 | http://arxiv.org/abs/1806.00548v4 | http://arxiv.org/pdf/1806.00548v4.pdf | https://github.com/QData/JEEK | true | false | true | none |
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | 1703.10593 | https://arxiv.org/abs/1703.10593v7 | https://arxiv.org/pdf/1703.10593v7.pdf | https://github.com/WeiYangze/hibernate-demo | false | false | true | tf |
https://paperswithcode.com/paper/neural-machine-translation-of-rare-words-with | Neural Machine Translation of Rare Words with Subword Units | 1508.07909 | http://arxiv.org/abs/1508.07909v5 | http://arxiv.org/pdf/1508.07909v5.pdf | https://github.com/simonjisu/NMT | false | false | true | pytorch |
https://paperswithcode.com/paper/breaking-the-nonsmooth-barrier-a-scalable | Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization | 1707.06468 | http://arxiv.org/abs/1707.06468v3 | http://arxiv.org/pdf/1707.06468v3.pdf | https://github.com/fabianp/ProxASAGA | true | true | true | none |
https://paperswithcode.com/paper/underground-root-tuber-sensing-via-a-wi-fi | Underground Root Tuber Sensing via a Wi-Fi Mesh Network | null | https://dl.acm.org/doi/10.1145/3715014.3724365 | https://dl.acm.org/doi/pdf/10.1145/3715014.3724365 | https://github.com/Data-driven-RTI/undergroud_sensing_wifi_csi | false | false | false | pytorch |
https://paperswithcode.com/paper/like-what-you-like-knowledge-distill-via | Like What You Like: Knowledge Distill via Neuron Selectivity Transfer | 1707.01219 | http://arxiv.org/abs/1707.01219v2 | http://arxiv.org/pdf/1707.01219v2.pdf | https://github.com/TuSimple/neuron-selectivity-transfer | false | false | true | tf |
https://paperswithcode.com/paper/reassessing-the-goals-of-grammatical-error | Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality | null | https://aclanthology.org/Q16-1013 | https://aclanthology.org/Q16-1013.pdf | https://github.com/keisks/reassess-gec | true | true | false | none |
https://paperswithcode.com/paper/ctcmodel-a-keras-model-for-connectionist | CTCModel: a Keras Model for Connectionist Temporal Classification | 1901.07957 | http://arxiv.org/abs/1901.07957v1 | http://arxiv.org/pdf/1901.07957v1.pdf | https://github.com/cyprienruffino/CTCModel | false | false | true | tf |
https://paperswithcode.com/paper/alternating-direction-graph-matching | Alternating Direction Graph Matching | 1611.07583 | http://arxiv.org/abs/1611.07583v4 | http://arxiv.org/pdf/1611.07583v4.pdf | https://github.com/netw0rkf10w/adgm | false | false | false | none |
https://paperswithcode.com/paper/efficient-neural-architecture-search-via-1 | Efficient Neural Architecture Search via Parameter Sharing | 1802.03268 | http://arxiv.org/abs/1802.03268v2 | http://arxiv.org/pdf/1802.03268v2.pdf | https://github.com/Ezereal/enas | false | false | true | tf |
https://paperswithcode.com/paper/forgetting-to-learn-logic-programs | Forgetting to learn logic programs | 1911.06643 | https://arxiv.org/abs/1911.06643v1 | https://arxiv.org/pdf/1911.06643v1.pdf | https://github.com/metagol/metagol | true | true | false | none |
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/gpandu/Object-detection | false | false | true | tf |
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection | Focal Loss for Dense Object Detection | 1708.02002 | http://arxiv.org/abs/1708.02002v2 | http://arxiv.org/pdf/1708.02002v2.pdf | https://github.com/neshitov/UNet | false | false | true | pytorch |
https://paperswithcode.com/paper/unsupervised-adaptation-learning-for | Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.pdf | https://github.com/JiangtaoNie/UAL | true | true | false | pytorch |
https://paperswithcode.com/paper/fast-mser | Fast MSER | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Fast_MSER_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Fast_MSER_CVPR_2020_paper.pdf | https://github.com/mmmn143/fast-mser | true | true | false | none |
https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep | Aggregated Residual Transformations for Deep Neural Networks | 1611.05431 | http://arxiv.org/abs/1611.05431v2 | http://arxiv.org/pdf/1611.05431v2.pdf | https://github.com/TuSimple/resnet.mxnet | false | false | true | tf |
https://paperswithcode.com/paper/backpack-packing-more-into-backprop | BackPACK: Packing more into backprop | 1912.10985 | https://arxiv.org/abs/1912.10985v2 | https://arxiv.org/pdf/1912.10985v2.pdf | https://github.com/f-dangel/backpack | false | false | false | pytorch |
https://paperswithcode.com/paper/linear-colour-segmentation-revisited | Linear colour segmentation revisited | 1901.00534 | http://arxiv.org/abs/1901.00534v1 | http://arxiv.org/pdf/1901.00534v1.pdf | https://github.com/visillect/colorsegdataset | true | true | false | none |
https://paperswithcode.com/paper/guided-image-generation-with-conditional | Guided Image Generation with Conditional Invertible Neural Networks | 1907.02392 | https://arxiv.org/abs/1907.02392v3 | https://arxiv.org/pdf/1907.02392v3.pdf | https://github.com/5yearsKim/Conditional-Normalizing-Flow | false | false | true | pytorch |
https://paperswithcode.com/paper/glow-generative-flow-with-invertible-1x1 | Glow: Generative Flow with Invertible 1x1 Convolutions | 1807.03039 | http://arxiv.org/abs/1807.03039v2 | http://arxiv.org/pdf/1807.03039v2.pdf | https://github.com/5yearsKim/Conditional-Normalizing-Flow | false | false | true | pytorch |
https://paperswithcode.com/paper/the-simple-essence-of-automatic | The simple essence of automatic differentiation | 1804.00746 | http://arxiv.org/abs/1804.00746v2 | http://arxiv.org/pdf/1804.00746v2.pdf | https://github.com/conal/essence-of-ad | false | false | true | none |
https://paperswithcode.com/paper/an-ensemble-model-of-word-based-and-character | An Ensemble Model of Word-based and Character-based Models for Japanese and Chinese Input Method | null | https://aclanthology.org/W12-4802 | https://aclanthology.org/W12-4802.pdf | https://github.com/nokuno/jsc | true | true | false | none |
https://paperswithcode.com/paper/meta-transfer-networks-for-zero-shot-learning | Episode-based Prototype Generating Network for Zero-Shot Learning | 1909.03360 | https://arxiv.org/abs/1909.03360v2 | https://arxiv.org/pdf/1909.03360v2.pdf | https://github.com/yunlongyu/EPGN | true | true | false | tf |
https://paperswithcode.com/paper/spin-orientations-of-merging-black-holes | Spin orientations of merging black holes formed from the evolution of stellar binaries | 1808.02491 | http://arxiv.org/abs/1808.02491v1 | http://arxiv.org/pdf/1808.02491v1.pdf | https://github.com/dgerosa/spops | true | true | false | none |
https://paperswithcode.com/paper/constrained-size-tensorflow-models-for | Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge | 1808.06739 | http://arxiv.org/abs/1808.06739v3 | http://arxiv.org/pdf/1808.06739v3.pdf | https://github.com/boliu61/youtube-8m | 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/Gaurav927/Neural_Style_Transfer | false | false | true | pytorch |
https://paperswithcode.com/paper/easy-transfer-learning-by-exploiting-intra | Easy Transfer Learning By Exploiting Intra-domain Structures | 1904.01376 | http://arxiv.org/abs/1904.01376v2 | http://arxiv.org/pdf/1904.01376v2.pdf | https://github.com/jindongwang/transferlearning | false | false | true | pytorch |
https://paperswithcode.com/paper/convex-space-learning-for-tabular-synthetic | Convex space learning for tabular synthetic data generation | 2407.09789 | https://arxiv.org/abs/2407.09789v1 | https://arxiv.org/pdf/2407.09789v1.pdf | https://github.com/manjunath-mahendra/NextConvGeN | true | false | false | tf |
https://paperswithcode.com/paper/giraffe-using-deep-reinforcement-learning-to | Giraffe: Using Deep Reinforcement Learning to Play Chess | 1509.01549 | http://arxiv.org/abs/1509.01549v2 | http://arxiv.org/pdf/1509.01549v2.pdf | https://github.com/saikrishna-1996/deep_pepper_chess | false | false | true | pytorch |
https://paperswithcode.com/paper/13013666 | Zero-Shot Learning Through Cross-Modal Transfer | 1301.3666 | http://arxiv.org/abs/1301.3666v2 | http://arxiv.org/pdf/1301.3666v2.pdf | https://github.com/mganjoo/zslearning | false | false | false | none |
https://paperswithcode.com/paper/semantic-image-synthesis-via-adversarial | Semantic Image Synthesis via Adversarial Learning | 1707.06873 | http://arxiv.org/abs/1707.06873v1 | http://arxiv.org/pdf/1707.06873v1.pdf | https://github.com/vtddggg/BilinearGAN_for_LBIE | false | false | true | pytorch |
https://paperswithcode.com/paper/composition-based-crystal-materials-symmetry | Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors | 2105.07303 | https://arxiv.org/abs/2105.07303v1 | https://arxiv.org/pdf/2105.07303v1.pdf | https://github.com/Yuxinya/SG_predict | true | true | false | none |
https://paperswithcode.com/paper/generative-adversarial-text-to-image | Generative Adversarial Text to Image Synthesis | 1605.05396 | http://arxiv.org/abs/1605.05396v2 | http://arxiv.org/pdf/1605.05396v2.pdf | https://github.com/vtddggg/BilinearGAN_for_LBIE | false | false | true | pytorch |
https://paperswithcode.com/paper/mixup-beyond-empirical-risk-minimization | mixup: Beyond Empirical Risk Minimization | 1710.09412 | http://arxiv.org/abs/1710.09412v2 | http://arxiv.org/pdf/1710.09412v2.pdf | https://github.com/simongrest/kaggle-freesound-audio-tagging-2019 | false | false | true | none |
https://paperswithcode.com/paper/metasci-scalable-and-adaptive-reconstruction | MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing | 2103.01786 | https://arxiv.org/abs/2103.01786v1 | https://arxiv.org/pdf/2103.01786v1.pdf | https://github.com/xyvirtualgroup/MetaSCI-CVPR2021 | true | true | false | 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/simongrest/kaggle-freesound-audio-tagging-2019 | false | false | true | none |
https://paperswithcode.com/paper/open3d-a-modern-library-for-3d-data | Open3D: A Modern Library for 3D Data Processing | 1801.09847 | http://arxiv.org/abs/1801.09847v1 | http://arxiv.org/pdf/1801.09847v1.pdf | https://github.com/IntelVCL/Open3D | false | false | true | tf |
https://paperswithcode.com/paper/trainable-frontend-for-robust-and-far-field | Trainable Frontend For Robust and Far-Field Keyword Spotting | 1607.05666 | http://arxiv.org/abs/1607.05666v1 | http://arxiv.org/pdf/1607.05666v1.pdf | https://github.com/simongrest/kaggle-freesound-audio-tagging-2019 | false | false | true | none |
https://paperswithcode.com/paper/xdeepfm-combining-explicit-and-implicit | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | 1803.05170 | http://arxiv.org/abs/1803.05170v3 | http://arxiv.org/pdf/1803.05170v3.pdf | https://github.com/bettenW/Tencent2019_Finals_Rank1st | false | false | true | tf |
https://paperswithcode.com/paper/real-time-localization-and-tracking-of | Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV | 1712.01491 | http://arxiv.org/abs/1712.01491v4 | http://arxiv.org/pdf/1712.01491v4.pdf | https://github.com/AdelaideAuto-IDLab/TrackerBots/tree/master/JoFR_2019 | true | false | false | none |
https://paperswithcode.com/paper/adversarial-autoencoders | Adversarial Autoencoders | 1511.05644 | http://arxiv.org/abs/1511.05644v2 | http://arxiv.org/pdf/1511.05644v2.pdf | https://github.com/santi-pdp/pase | false | false | true | pytorch |
https://paperswithcode.com/paper/accelerating-deep-unsupervised-domain | Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning | 1904.02654 | http://arxiv.org/abs/1904.02654v1 | http://arxiv.org/pdf/1904.02654v1.pdf | https://github.com/jindongwang/transferlearning | true | true | true | pytorch |
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | 1506.01497 | http://arxiv.org/abs/1506.01497v3 | http://arxiv.org/pdf/1506.01497v3.pdf | https://github.com/zacks417/faster-rcnn-tf | false | false | true | tf |
https://paperswithcode.com/paper/stein-variational-gradient-descent-a-general | Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm | 1608.04471 | https://arxiv.org/abs/1608.04471v3 | https://arxiv.org/pdf/1608.04471v3.pdf | https://github.com/activatedgeek/stein-gradient | false | false | false | pytorch |
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection | Focal Loss for Dense Object Detection | 1708.02002 | http://arxiv.org/abs/1708.02002v2 | http://arxiv.org/pdf/1708.02002v2.pdf | https://github.com/vantupham/darknet | false | false | true | none |
https://paperswithcode.com/paper/context-aware-attentive-knowledge-tracing | Context-Aware Attentive Knowledge Tracing | 2007.12324 | https://arxiv.org/abs/2007.12324v1 | https://arxiv.org/pdf/2007.12324v1.pdf | https://github.com/arghosh/AKT | true | true | true | pytorch |
https://paperswithcode.com/paper/efficient-estimation-of-word-representations | Efficient Estimation of Word Representations in Vector Space | 1301.3781 | http://arxiv.org/abs/1301.3781v3 | http://arxiv.org/pdf/1301.3781v3.pdf | https://github.com/rohith2506/word_embeddings | false | false | true | none |
https://paperswithcode.com/paper/compressing-physical-properties-of-atomic | Compressing physical properties of atomic species for improving predictive chemistry | 1811.00123 | http://arxiv.org/abs/1811.00123v1 | http://arxiv.org/pdf/1811.00123v1.pdf | https://github.com/jeherr/element-encoder | false | false | true | tf |
https://paperswithcode.com/paper/contour-knowledge-transfer-for-salient-object | Contour Knowledge Transfer for Salient Object Detection | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf | https://github.com/lixin666/C2SNet | true | true | false | none |
https://paperswithcode.com/paper/universal-language-model-fine-tuning-for-text | Universal Language Model Fine-tuning for Text Classification | 1801.06146 | http://arxiv.org/abs/1801.06146v5 | http://arxiv.org/pdf/1801.06146v5.pdf | https://github.com/comicencyclo/TransferLearning_DiscriminativeFineTuning | false | false | true | none |
https://paperswithcode.com/paper/restoring-negative-information-in-few-shot | Restoring Negative Information in Few-Shot Object Detection | 2010.11714 | https://arxiv.org/abs/2010.11714v2 | https://arxiv.org/pdf/2010.11714v2.pdf | https://github.com/yang-yk/NP-RepMet | true | true | false | mxnet |
https://paperswithcode.com/paper/predictive-entropy-search-for-bayesian | Predictive Entropy Search for Bayesian Optimization with Unknown Constraints | 1502.05312 | http://arxiv.org/abs/1502.05312v2 | http://arxiv.org/pdf/1502.05312v2.pdf | https://github.com/chongkewu/PESC-HPC | false | false | true | none |
https://paperswithcode.com/paper/real-time-air-pollution-prediction-model | Real-time Air Pollution prediction model based on Spatiotemporal Big data | 1805.00432 | http://arxiv.org/abs/1805.00432v3 | http://arxiv.org/pdf/1805.00432v3.pdf | https://github.com/vanduc103/air_analysis_v1 | true | true | false | tf |
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection | Focal Loss for Dense Object Detection | 1708.02002 | http://arxiv.org/abs/1708.02002v2 | http://arxiv.org/pdf/1708.02002v2.pdf | https://github.com/yhenon/pytorch-retinanet | false | false | true | pytorch |
https://paperswithcode.com/paper/learning-spatiotemporal-features-with-3d | Learning Spatiotemporal Features with 3D Convolutional Networks | 1412.0767 | http://arxiv.org/abs/1412.0767v4 | http://arxiv.org/pdf/1412.0767v4.pdf | https://github.com/AKASH2907/Content-based-Video-Recommendation | false | false | true | tf |
https://paperswithcode.com/paper/increasingly-packing-multiple-facial | Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning | null | https://dl.acm.org/doi/10.1145/3323873.3325053 | https://dl.acm.org/doi/pdf/10.1145/3323873.3325053 | https://github.com/ivclab/CPG | false | false | false | pytorch |
https://paperswithcode.com/paper/integralaction-pose-driven-feature | IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos | 2007.06317 | https://arxiv.org/abs/2007.06317v2 | https://arxiv.org/pdf/2007.06317v2.pdf | https://github.com/arunos728/arunos728.github.io | false | false | true | none |
https://paperswithcode.com/paper/fully-convolutional-pixel-adaptive-image | Fully Convolutional Pixel Adaptive Image Denoiser | 1807.07569 | https://arxiv.org/abs/1807.07569v4 | https://arxiv.org/pdf/1807.07569v4.pdf | https://github.com/csm9493/FC-AIDE | false | false | true | tf |
https://paperswithcode.com/paper/joint-3d-face-reconstruction-and-dense | Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network | 1803.07835 | http://arxiv.org/abs/1803.07835v1 | http://arxiv.org/pdf/1803.07835v1.pdf | https://github.com/jimmy0087/faceai-master | false | false | true | tf |
https://paperswithcode.com/paper/implicit-self-consistent-description-of | Implicit self-consistent description of electrolyte in plane-wave density-functional theory | 1601.03346 | http://arxiv.org/abs/1601.03346v1 | http://arxiv.org/pdf/1601.03346v1.pdf | https://github.com/henniggroup/VASPsol | true | true | true | none |
https://paperswithcode.com/paper/news-headline-grouping-as-a-challenging-nlu-1 | News Headline Grouping as a Challenging NLU Task | 2105.05391 | https://arxiv.org/abs/2105.05391v1 | https://arxiv.org/pdf/2105.05391v1.pdf | https://github.com/tingofurro/headline_grouping | true | true | false | pytorch |
https://paperswithcode.com/paper/end-to-end-learning-of-lda-by-mirror-descent | End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture | 1508.03398 | http://arxiv.org/abs/1508.03398v2 | http://arxiv.org/pdf/1508.03398v2.pdf | https://github.com/jvking/bp-lda | true | true | false | none |
https://paperswithcode.com/paper/an-optimization-approach-to-learning-falling | An Optimization Approach to Learning Falling Rule Lists | 1710.02572 | http://arxiv.org/abs/1710.02572v3 | http://arxiv.org/pdf/1710.02572v3.pdf | https://github.com/cfchen-duke/FRLOptimization | true | true | false | none |
https://paperswithcode.com/paper/dueling-network-architectures-for-deep | Dueling Network Architectures for Deep Reinforcement Learning | 1511.06581 | http://arxiv.org/abs/1511.06581v3 | http://arxiv.org/pdf/1511.06581v3.pdf | https://github.com/wtingda/DeepRLBreakout | false | false | true | tf |
https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement | Asynchronous Methods for Deep Reinforcement Learning | 1602.01783 | http://arxiv.org/abs/1602.01783v2 | http://arxiv.org/pdf/1602.01783v2.pdf | https://github.com/wtingda/DeepRLBreakout | false | false | true | tf |
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