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/low-rank-adapting-models-for-sparse | Low-Rank Adapting Models for Sparse Autoencoders | 2501.19406 | https://arxiv.org/abs/2501.19406v1 | https://arxiv.org/pdf/2501.19406v1.pdf | https://github.com/adamkarvonen/sae_kl_finetune | false | false | true | jax |
https://paperswithcode.com/paper/learning-discriminative-features-with | Learning Discriminative Features with Multiple Granularities for Person Re-Identification | 1804.01438 | http://arxiv.org/abs/1804.01438v3 | http://arxiv.org/pdf/1804.01438v3.pdf | https://github.com/zp1018/ReID-MGN | false | false | true | pytorch |
https://paperswithcode.com/paper/squeezenet-alexnet-level-accuracy-with-50x | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size | 1602.07360 | http://arxiv.org/abs/1602.07360v4 | http://arxiv.org/pdf/1602.07360v4.pdf | https://github.com/DT42/squeezenet_demo | true | true | true | none |
https://paperswithcode.com/paper/transformation-of-wiktionary-entry-structure | Transformation of Wiktionary entry structure into tables and relations in a relational database schema | 1011.1368 | http://arxiv.org/abs/1011.1368v1 | http://arxiv.org/pdf/1011.1368v1.pdf | https://github.com/componavt/wikokit | false | false | true | none |
https://paperswithcode.com/paper/time-expressions-in-mental-health-records-for | Time Expressions in Mental Health Records for Symptom Onset Extraction | null | https://aclanthology.org/W18-5621 | https://aclanthology.org/W18-5621.pdf | https://github.com/medesto/systems-adaptation | true | true | false | none |
https://paperswithcode.com/paper/metrical-accent-aware-vocal-onset-detection | Metrical-accent Aware Vocal Onset Detection in Polyphonic Audio | 1707.06163 | http://arxiv.org/abs/1707.06163v1 | http://arxiv.org/pdf/1707.06163v1.pdf | https://github.com/georgid/lakh_vocal_segments_dataset | true | true | false | none |
https://paperswithcode.com/paper/using-deep-neural-networks-to-predict-and | Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes | 2105.04922 | https://arxiv.org/abs/2105.04922v1 | https://arxiv.org/pdf/2105.04922v1.pdf | https://github.com/brain-bzh/PolarCodesDNN | true | true | false | none |
https://paperswithcode.com/paper/automatic-guide-generation-for-stan-via | Automatic Guide Generation for Stan via NumPyro | 2110.11790 | https://arxiv.org/abs/2110.11790v1 | https://arxiv.org/pdf/2110.11790v1.pdf | https://github.com/deepppl/evaluation-autoguide | true | true | false | jax |
https://paperswithcode.com/paper/free-form-image-inpainting-with-gated | Free-Form Image Inpainting with Gated Convolution | 1806.03589 | https://arxiv.org/abs/1806.03589v2 | https://arxiv.org/pdf/1806.03589v2.pdf | https://github.com/avalonstrel/GatedConvolution_pytorch | false | false | false | pytorch |
https://paperswithcode.com/paper/semi-supervised-deep-learning-for-fully | Semi-Supervised Deep Learning for Fully Convolutional Networks | 1703.06000 | http://arxiv.org/abs/1703.06000v2 | http://arxiv.org/pdf/1703.06000v2.pdf | https://github.com/bumuckl/SemiSupervisedDLForFCNs | false | false | true | none |
https://paperswithcode.com/paper/on-learning-paradigms-for-the-travelling | On Learning Paradigms for the Travelling Salesman Problem | 1910.07210 | https://arxiv.org/abs/1910.07210v2 | https://arxiv.org/pdf/1910.07210v2.pdf | https://github.com/chaitjo/graph-convnet-tsp | false | false | true | pytorch |
https://paperswithcode.com/paper/recurrent-neural-networks-for-polyphonic | Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings | 1604.00861 | http://arxiv.org/abs/1604.00861v1 | http://arxiv.org/pdf/1604.00861v1.pdf | https://github.com/yardencsGitHub/tweetynet | false | false | true | tf |
https://paperswithcode.com/paper/an-end-to-end-trainable-neural-network-for | An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition | 1507.05717 | http://arxiv.org/abs/1507.05717v1 | http://arxiv.org/pdf/1507.05717v1.pdf | https://github.com/wacr2008/tensorflow_crnn | false | false | true | tf |
https://paperswithcode.com/paper/ultra-lightweight-image-super-resolution-with | Multi-Attention Based Ultra Lightweight Image Super-Resolution | 2008.12912 | https://arxiv.org/abs/2008.12912v2 | https://arxiv.org/pdf/2008.12912v2.pdf | https://github.com/AbdulMoqeet/MAFFSRN | false | false | true | pytorch |
https://paperswithcode.com/paper/a-computational-analysis-of-financial-and | A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors | null | https://aclanthology.org/2020.fnp-1.31 | https://aclanthology.org/2020.fnp-1.31.pdf | https://github.com/forgefin/fin-env-narrative | true | true | false | tf |
https://paperswithcode.com/paper/distributed-control-of-descriptor-networks-a | Distributed Control of Descriptor Networks: A Convex Procedure for Augmented Sparsity | 2109.05954 | https://arxiv.org/abs/2109.05954v9 | https://arxiv.org/pdf/2109.05954v9.pdf | https://github.com/AndreiSperila/CONPRAS | true | true | true | none |
https://paperswithcode.com/paper/finding-archetypal-spaces-for-data-using | Finding Archetypal Spaces Using Neural Networks | 1901.09078 | https://arxiv.org/abs/1901.09078v2 | https://arxiv.org/pdf/1901.09078v2.pdf | https://github.com/KrishnaswamyLab/AAnet | true | true | true | tf |
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/raferguson/CNN-Profile-Picture | false | false | true | tf |
https://paperswithcode.com/paper/adversarial-learning-for-semi-supervised | Adversarial Learning for Semi-Supervised Semantic Segmentation | 1802.07934 | http://arxiv.org/abs/1802.07934v2 | http://arxiv.org/pdf/1802.07934v2.pdf | https://github.com/hfslyc/AdvSemiSeg | true | true | true | pytorch |
https://paperswithcode.com/paper/semi-supervised-classification-with-graph | Semi-Supervised Classification with Graph Convolutional Networks | 1609.02907 | http://arxiv.org/abs/1609.02907v4 | http://arxiv.org/pdf/1609.02907v4.pdf | https://github.com/selmiss/gp-tlstgcn | false | false | true | pytorch |
https://paperswithcode.com/paper/tensor-renormalization-group-approach-to-2d | Tensor renormalization group approach to 2D classical lattice models | cond-mat/0611687 | https://arxiv.org/abs/cond-mat/0611687v2 | https://arxiv.org/pdf/cond-mat/0611687v2.pdf | https://github.com/mhauru/abeliantensors | false | false | true | none |
https://paperswithcode.com/paper/rotation-method-for-accelerating-multiple | Rotation method for accelerating multiple-spherical Bessel function integrals against a numerical source function | 1912.00065 | https://arxiv.org/abs/1912.00065v1 | https://arxiv.org/pdf/1912.00065v1.pdf | https://github.com/eelregit/sbf_rotation | true | true | true | none |
https://paperswithcode.com/paper/shared-representational-geometry-across | Shared Representational Geometry Across Neural Networks | 1811.11684 | http://arxiv.org/abs/1811.11684v2 | http://arxiv.org/pdf/1811.11684v2.pdf | https://github.com/qihongl/nnsrm-neurips18 | true | true | true | none |
https://paperswithcode.com/paper/openfermion-the-electronic-structure-package | OpenFermion: The Electronic Structure Package for Quantum Computers | 1710.07629 | http://arxiv.org/abs/1710.07629v3 | http://arxiv.org/pdf/1710.07629v3.pdf | https://github.com/quantumlib/OpenFermion-Cirq | false | false | true | none |
https://paperswithcode.com/paper/least-dependent-component-analysis-based-on | Least Dependent Component Analysis Based on Mutual Information | physics/0405044 | https://arxiv.org/abs/physics/0405044v2 | https://arxiv.org/pdf/physics/0405044v2.pdf | https://github.com/nordavinden/Mutual-Information-ICA | false | false | true | none |
https://paperswithcode.com/paper/estimating-mutual-information | Estimating Mutual Information | cond-mat/0305641 | https://arxiv.org/abs/cond-mat/0305641v1 | https://arxiv.org/pdf/cond-mat/0305641v1.pdf | https://github.com/nordavinden/Mutual-Information-ICA | false | false | true | none |
https://paperswithcode.com/paper/a-style-based-generator-architecture-for | A Style-Based Generator Architecture for Generative Adversarial Networks | 1812.04948 | http://arxiv.org/abs/1812.04948v3 | http://arxiv.org/pdf/1812.04948v3.pdf | https://github.com/pfnet-research/chainer-stylegan | false | false | true | none |
https://paperswithcode.com/paper/fracbnn-accurate-and-fpga-efficient-binary | FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations | 2012.12206 | https://arxiv.org/abs/2012.12206v1 | https://arxiv.org/pdf/2012.12206v1.pdf | https://github.com/ychzhang/fracbnn | false | false | true | pytorch |
https://paperswithcode.com/paper/150501303 | XTreePath: A generalization of XPath to handle real world structural variation | 1505.01303 | http://arxiv.org/abs/1505.01303v3 | http://arxiv.org/pdf/1505.01303v3.pdf | https://github.com/ieee8023/XTreePath | false | false | true | none |
https://paperswithcode.com/paper/noise2noise-learning-image-restoration | Noise2Noise: Learning Image Restoration without Clean Data | 1803.04189 | http://arxiv.org/abs/1803.04189v3 | http://arxiv.org/pdf/1803.04189v3.pdf | https://github.com/itsuki8914/simply-noise2noise-TF | false | false | true | tf |
https://paperswithcode.com/paper/pointhop-an-explainable-machine-learning | PointHop: An Explainable Machine Learning Method for Point Cloud Classification | 1907.12766 | https://arxiv.org/abs/1907.12766v2 | https://arxiv.org/pdf/1907.12766v2.pdf | https://github.com/minzhang-1/PointHop | false | false | true | pytorch |
https://paperswithcode.com/paper/end-to-end-recovery-of-human-shape-and-pose | End-to-end Recovery of Human Shape and Pose | 1712.06584 | http://arxiv.org/abs/1712.06584v2 | http://arxiv.org/pdf/1712.06584v2.pdf | https://github.com/MandyMo/pytorch_HMR | false | false | true | pytorch |
https://paperswithcode.com/paper/a-style-based-generator-architecture-for | A Style-Based Generator Architecture for Generative Adversarial Networks | 1812.04948 | http://arxiv.org/abs/1812.04948v3 | http://arxiv.org/pdf/1812.04948v3.pdf | https://github.com/itsuki8914/stylegan-TensorFlow | false | false | true | tf |
https://paperswithcode.com/paper/sparsh-amg-a-library-for-hybrid-cpu-gpu | SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods | 2007.00056 | https://arxiv.org/abs/2007.00056v1 | https://arxiv.org/pdf/2007.00056v1.pdf | https://github.com/cmgcds/SParSH-AMG | false | false | true | none |
https://paperswithcode.com/paper/the-comparison-of-wiktionary-thesauri | The comparison of Wiktionary thesauri transformed into the machine-readable format | 1006.5040 | http://arxiv.org/abs/1006.5040v1 | http://arxiv.org/pdf/1006.5040v1.pdf | https://github.com/componavt/wikokit | false | false | true | none |
https://paperswithcode.com/paper/deep-high-resolution-representation-learning | Deep High-Resolution Representation Learning for Human Pose Estimation | 1902.09212 | http://arxiv.org/abs/1902.09212v1 | http://arxiv.org/pdf/1902.09212v1.pdf | https://github.com/HRNet/HRNet-Image-Classification | false | false | true | pytorch |
https://paperswithcode.com/paper/high-resolution-representations-for-labeling | High-Resolution Representations for Labeling Pixels and Regions | 1904.04514 | http://arxiv.org/abs/1904.04514v1 | http://arxiv.org/pdf/1904.04514v1.pdf | https://github.com/HRNet/HRNet-Image-Classification | false | false | true | pytorch |
https://paperswithcode.com/paper/faldoi-a-new-minimization-strategy-for-large | FALDOI: A new minimization strategy for large displacement variational optical flow | 1602.08960 | http://arxiv.org/abs/1602.08960v3 | http://arxiv.org/pdf/1602.08960v3.pdf | https://github.com/fperezgamonal/faldoi-ipol | false | false | true | none |
https://paperswithcode.com/paper/metadata-embeddings-for-user-and-item-cold | Metadata Embeddings for User and Item Cold-start Recommendations | 1507.08439 | http://arxiv.org/abs/1507.08439v1 | http://arxiv.org/pdf/1507.08439v1.pdf | https://github.com/lyst/lightfm-paper | true | true | true | none |
https://paperswithcode.com/paper/unified-treatment-of-the-asymptotics-of | Unified treatment of the asymptotics of asymmetric kernel density estimators | 1512.03188 | http://arxiv.org/abs/1512.03188v1 | http://arxiv.org/pdf/1512.03188v1.pdf | https://github.com/tommyod/KDEpy | false | false | true | none |
https://paperswithcode.com/paper/road-extraction-by-deep-residual-u-net | Road Extraction by Deep Residual U-Net | 1711.10684 | http://arxiv.org/abs/1711.10684v1 | http://arxiv.org/pdf/1711.10684v1.pdf | https://github.com/Kaido0/Brain-Tissue-Segment-Keras | false | false | true | tf |
https://paperswithcode.com/paper/realtime-multi-person-2d-pose-estimation | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields | 1611.08050 | http://arxiv.org/abs/1611.08050v2 | http://arxiv.org/pdf/1611.08050v2.pdf | https://github.com/lncarter/Openpose | false | false | true | pytorch |
https://paperswithcode.com/paper/hand-keypoint-detection-in-single-images | Hand Keypoint Detection in Single Images using Multiview Bootstrapping | 1704.07809 | http://arxiv.org/abs/1704.07809v1 | http://arxiv.org/pdf/1704.07809v1.pdf | https://github.com/lncarter/Openpose | false | false | true | pytorch |
https://paperswithcode.com/paper/convolutional-pose-machines | Convolutional Pose Machines | 1602.00134 | http://arxiv.org/abs/1602.00134v4 | http://arxiv.org/pdf/1602.00134v4.pdf | https://github.com/lncarter/Openpose | false | false | true | pytorch |
https://paperswithcode.com/paper/exploiting-kernel-sparsity-and-entropy-for | Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression | 1812.04368 | http://arxiv.org/abs/1812.04368v2 | http://arxiv.org/pdf/1812.04368v2.pdf | https://github.com/yuchaoli/KSE | true | true | false | pytorch |
https://paperswithcode.com/paper/blockchain-and-trusted-computing-problems | Blockchain and Trusted Computing: Problems, Pitfalls, and a Solution for Hyperledger Fabric | 1805.08541 | http://arxiv.org/abs/1805.08541v1 | http://arxiv.org/pdf/1805.08541v1.pdf | https://github.com/hyperledger-labs/fabric-private-chaincode | false | false | true | none |
https://paperswithcode.com/paper/emotionflow-capture-the-dialogue-level | EmotionFlow: Capture the Dialogue Level Emotion Transitions | null | https://github.com/fpcsong/emotionflow/blob/master/EmotionFlow.pdf | https://github.com/fpcsong/emotionflow/blob/master/EmotionFlow.pdf | https://github.com/fpcsong/emotionflow | false | false | false | pytorch |
https://paperswithcode.com/paper/neural-code-search-revisited-enhancing-code | Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent | 2008.12193 | https://arxiv.org/abs/2008.12193v1 | https://arxiv.org/pdf/2008.12193v1.pdf | https://github.com/nokia/codesearch | true | true | true | none |
https://paperswithcode.com/paper/pytorch-biggraph-a-large-scale-graph | PyTorch-BigGraph: A Large-scale Graph Embedding System | 1903.12287 | http://arxiv.org/abs/1903.12287v3 | http://arxiv.org/pdf/1903.12287v3.pdf | https://github.com/facebookresearch/PyTorch-BigGraph | false | true | false | pytorch |
https://paperswithcode.com/paper/end-to-end-memory-networks | End-To-End Memory Networks | 1503.08895 | http://arxiv.org/abs/1503.08895v5 | http://arxiv.org/pdf/1503.08895v5.pdf | https://github.com/aadil-srivastava01/End-To-End-Memory-Networks | false | false | true | none |
https://paperswithcode.com/paper/squeezenet-alexnet-level-accuracy-with-50x | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size | 1602.07360 | http://arxiv.org/abs/1602.07360v4 | http://arxiv.org/pdf/1602.07360v4.pdf | https://github.com/Kaido0/Brain-Tissue-Segment-Keras | false | false | true | tf |
https://paperswithcode.com/paper/unsupervised-pixel-level-domain-adaptation | Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks | 1612.05424 | http://arxiv.org/abs/1612.05424v2 | http://arxiv.org/pdf/1612.05424v2.pdf | https://github.com/tensorflow/models/tree/master/research/domain_adaptation | false | false | true | tf |
https://paperswithcode.com/paper/reliable-local-explanations-for-machine | Reliable Local Explanations for Machine Listening | 2005.07788 | https://arxiv.org/abs/2005.07788v1 | https://arxiv.org/pdf/2005.07788v1.pdf | https://github.com/saum25/local_exp_robustness | true | true | false | tf |
https://paperswithcode.com/paper/defense-against-adversarial-attacks-using | Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser | 1712.02976 | http://arxiv.org/abs/1712.02976v2 | http://arxiv.org/pdf/1712.02976v2.pdf | https://github.com/anishathalye/Guided-Denoise | false | false | true | tf |
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/xingxingyoulei/infer_cgan_onnx/tree/master/research/cv/CGAN | false | false | false | mindspore |
https://paperswithcode.com/paper/a-practical-guide-to-randomized-matrix | A Practical Guide to Randomized Matrix Computations with MATLAB Implementations | 1505.07570 | http://arxiv.org/abs/1505.07570v6 | http://arxiv.org/pdf/1505.07570v6.pdf | https://github.com/wangshusen/RandMatrixMatlab | true | true | true | none |
https://paperswithcode.com/paper/plsrglm-partial-least-squares-linear-and | plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R | 1810.01005 | http://arxiv.org/abs/1810.01005v1 | http://arxiv.org/pdf/1810.01005v1.pdf | https://github.com/fbertran/plsRglm | false | false | true | none |
https://paperswithcode.com/paper/a-multilayer-convolutional-encoder-decoder | A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction | 1801.08831 | http://arxiv.org/abs/1801.08831v1 | http://arxiv.org/pdf/1801.08831v1.pdf | https://github.com/seaweiqing/neuraltalk_plus_charcnn | false | false | true | tf |
https://paperswithcode.com/paper/unsupervised-domain-adaptation-for-medical | Unsupervised domain adaptation for medical imaging segmentation with self-ensembling | 1811.06042 | http://arxiv.org/abs/1811.06042v2 | http://arxiv.org/pdf/1811.06042v2.pdf | https://github.com/neuropoly/domainadaptation | true | true | false | pytorch |
https://paperswithcode.com/paper/overlapping-community-detection-at-scale-a | Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach | null | https://dl.acm.org/citation.cfm?id=2433471 | http://infolab.stanford.edu/~crucis/pubs/paper-nmfagm.pdf | https://github.com/benedekrozemberczki/karateclub | false | false | false | none |
https://paperswithcode.com/paper/the-loss-surfaces-of-multilayer-networks | The Loss Surfaces of Multilayer Networks | 1412.0233 | http://arxiv.org/abs/1412.0233v3 | http://arxiv.org/pdf/1412.0233v3.pdf | https://github.com/jchunn/Ambition | false | false | true | tf |
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