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/where-to-look-at-the-movies-analyzing-visual | Where to look at the movies : Analyzing visual attention to understand movie editing | 2102.13378 | https://arxiv.org/abs/2102.13378v1 | https://arxiv.org/pdf/2102.13378v1.pdf | https://github.com/abruckert/eye_tracking_filmmaking | true | true | false | none |
https://paperswithcode.com/paper/superaccurate-camera-calibration-via-inverse | Superaccurate Camera Calibration via Inverse Rendering | 2003.09177 | https://arxiv.org/abs/2003.09177v1 | https://arxiv.org/pdf/2003.09177v1.pdf | https://github.com/MortenHannemose/pytorch-vfi-cft | true | false | false | pytorch |
https://paperswithcode.com/paper/soccernet-a-scalable-dataset-for-action | SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos | 1804.04527 | http://arxiv.org/abs/1804.04527v2 | http://arxiv.org/pdf/1804.04527v2.pdf | https://github.com/SilvioGiancola/SoccerNet-code | false | false | false | tf |
https://paperswithcode.com/paper/fast-and-robust-multiple-colorchecker | Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks | 1810.08639 | http://arxiv.org/abs/1810.08639v1 | http://arxiv.org/pdf/1810.08639v1.pdf | https://github.com/pedrodiamel/colorchacker-detection | true | true | true | none |
https://paperswithcode.com/paper/skip-thought-vectors | Skip-Thought Vectors | 1506.06726 | http://arxiv.org/abs/1506.06726v1 | http://arxiv.org/pdf/1506.06726v1.pdf | https://github.com/facebookresearch/InferSent | false | false | true | pytorch |
https://paperswithcode.com/paper/vse-improving-visual-semantic-embeddings-with | VSE++: Improving Visual-Semantic Embeddings with Hard Negatives | 1707.05612 | http://arxiv.org/abs/1707.05612v4 | http://arxiv.org/pdf/1707.05612v4.pdf | https://github.com/armandvilalta/Full-network-multimodal-embeddings | false | false | true | none |
https://paperswithcode.com/paper/combining-monte-carlo-tree-search-and | Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring | 2304.12146 | https://arxiv.org/abs/2304.12146v1 | https://arxiv.org/pdf/2304.12146v1.pdf | https://github.com/cyril-grelier/gc_wvcp_mcts | true | true | false | none |
https://paperswithcode.com/paper/decision-stream-cultivating-deep-decision | Decision Stream: Cultivating Deep Decision Trees | 1704.07657 | http://arxiv.org/abs/1704.07657v3 | http://arxiv.org/pdf/1704.07657v3.pdf | https://github.com/aiff22/Decision-Stream | true | true | true | none |
https://paperswithcode.com/paper/leverage-eye-movement-data-for-saliency | How is Gaze Influenced by Image Transformations? Dataset and Model | 1905.06803 | https://arxiv.org/abs/1905.06803v4 | https://arxiv.org/pdf/1905.06803v4.pdf | https://github.com/CZHQuality/Sal-CFS-GAN | true | true | false | tf |
https://paperswithcode.com/paper/addressee-and-response-selection-in-multi | Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs | 1709.04005 | http://arxiv.org/abs/1709.04005v2 | http://arxiv.org/pdf/1709.04005v2.pdf | https://github.com/ryanzhumich/sirnn | true | true | true | none |
https://paperswithcode.com/paper/finite-sample-learning-of-moving-targets | Finite sample learning of moving targets | 2408.04406 | https://arxiv.org/abs/2408.04406v2 | https://arxiv.org/pdf/2408.04406v2.pdf | https://github.com/nikovert/finite-sample-learning-of-moving-targets | false | false | false | none |
https://paperswithcode.com/paper/interpret-federated-learning-with-shapley | Interpret Federated Learning with Shapley Values | 1905.04519 | https://arxiv.org/abs/1905.04519v1 | https://arxiv.org/pdf/1905.04519v1.pdf | https://github.com/crownpku/federated_shap | true | true | true | none |
https://paperswithcode.com/paper/lenient-multi-agent-deep-reinforcement | Lenient Multi-Agent Deep Reinforcement Learning | 1707.04402 | http://arxiv.org/abs/1707.04402v2 | http://arxiv.org/pdf/1707.04402v2.pdf | https://github.com/gjp1203/nui_in_madrl | false | false | true | none |
https://paperswithcode.com/paper/hierarchical-cross-modal-talking-face | Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss | 1905.03820 | https://arxiv.org/abs/1905.03820v1 | https://arxiv.org/pdf/1905.03820v1.pdf | https://github.com/lelechen63/ATVGnet | true | true | false | pytorch |
https://paperswithcode.com/paper/benchmarking-natural-language-understanding | Benchmarking Natural Language Understanding Services for building Conversational Agents | 1903.05566 | http://arxiv.org/abs/1903.05566v3 | http://arxiv.org/pdf/1903.05566v3.pdf | https://github.com/lackel/hierarchical_weighted_scl | false | false | true | pytorch |
https://paperswithcode.com/paper/hdltex-hierarchical-deep-learning-for-text | HDLTex: Hierarchical Deep Learning for Text Classification | 1709.08267 | http://arxiv.org/abs/1709.08267v2 | http://arxiv.org/pdf/1709.08267v2.pdf | https://github.com/lackel/hierarchical_weighted_scl | false | false | true | pytorch |
https://paperswithcode.com/paper/syntaxsqlnet-syntax-tree-networks-for-complex | SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task | 1810.05237 | http://arxiv.org/abs/1810.05237v2 | http://arxiv.org/pdf/1810.05237v2.pdf | https://github.com/heyanger/sqltools | false | false | true | none |
https://paperswithcode.com/paper/missing-data-infill-with-automunge-1 | Missing Data Infill with Automunge | 2202.09484 | https://arxiv.org/abs/2202.09484v1 | https://arxiv.org/pdf/2202.09484v1.pdf | https://github.com/gatorwatt/Paper_Demonstrations/tree/main/Missing_Data_infill | true | false | false | none |
https://paperswithcode.com/paper/temporal-attentive-alignment-for-video-domain | Temporal Attentive Alignment for Video Domain Adaptation | 1905.10861 | https://arxiv.org/abs/1905.10861v5 | https://arxiv.org/pdf/1905.10861v5.pdf | https://github.com/olivesgatech/TA3N | false | false | true | pytorch |
https://paperswithcode.com/paper/hybrid-reward-architecture-for-reinforcement | Hybrid Reward Architecture for Reinforcement Learning | 1706.04208 | http://arxiv.org/abs/1706.04208v2 | http://arxiv.org/pdf/1706.04208v2.pdf | https://github.com/KhenNguyn/DoAn3-MachineLearning | false | false | true | tf |
https://paperswithcode.com/paper/robustness-may-be-at-odds-with-accuracy | Robustness May Be at Odds with Accuracy | 1805.12152 | https://arxiv.org/abs/1805.12152v5 | https://arxiv.org/pdf/1805.12152v5.pdf | https://github.com/louis2889184/pytorch-adversarial-training | false | false | true | pytorch |
https://paperswithcode.com/paper/hierarchy-of-visual-words-a-learning-based | Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval | 1908.02786 | https://arxiv.org/abs/1908.02786v1 | https://arxiv.org/pdf/1908.02786v1.pdf | https://github.com/Prograf-UFF/HoVW | true | true | true | none |
https://paperswithcode.com/paper/deep-reinforcement-learning-from-human | Deep reinforcement learning from human preferences | 1706.03741 | https://arxiv.org/abs/1706.03741v4 | https://arxiv.org/pdf/1706.03741v4.pdf | https://github.com/vcharvet/project-rl | false | false | true | tf |
https://paperswithcode.com/paper/neural-motifs-scene-graph-parsing-with-global | Neural Motifs: Scene Graph Parsing with Global Context | 1711.06640 | http://arxiv.org/abs/1711.06640v2 | http://arxiv.org/pdf/1711.06640v2.pdf | https://github.com/HCPLab-SYSU/KERN | false | false | true | pytorch |
https://paperswithcode.com/paper/scene-relighting-with-illumination-estimation | Scene relighting with illumination estimation in the latent space on an encoder-decoder scheme | 2006.02333 | https://arxiv.org/abs/2006.02333v1 | https://arxiv.org/pdf/2006.02333v1.pdf | https://github.com/martin-ev/2DSceneRelighting | true | true | true | pytorch |
https://paperswithcode.com/paper/m-fuse-multi-frame-fusion-for-scene-flow | M-FUSE: Multi-frame Fusion for Scene Flow Estimation | 2207.05704 | https://arxiv.org/abs/2207.05704v2 | https://arxiv.org/pdf/2207.05704v2.pdf | https://github.com/cv-stuttgart/m-fuse | true | true | true | pytorch |
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/giovanniguidi/FCN-keras | false | false | true | none |
https://paperswithcode.com/paper/spurious-local-minima-are-common-in-two-layer | Spurious Local Minima are Common in Two-Layer ReLU Neural Networks | 1712.08968 | http://arxiv.org/abs/1712.08968v3 | http://arxiv.org/pdf/1712.08968v3.pdf | https://github.com/ItaySafran/OneLayerGDconvergence | true | true | false | none |
https://paperswithcode.com/paper/co-trained-convolutional-neural-networks-for | Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI | null | https://www.ncbi.nlm.nih.gov/pubmed/28850876 | https://www.ncbi.nlm.nih.gov/pubmed/28850876 | https://github.com/Andysis/co-trained-CADx | false | false | false | none |
https://paperswithcode.com/paper/improving-retinanet-for-ct-lesion-detection | Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels | 1906.02283 | https://arxiv.org/abs/1906.02283v1 | https://arxiv.org/pdf/1906.02283v1.pdf | https://github.com/fizyr/keras-retinanet | false | false | true | tf |
https://paperswithcode.com/paper/one-shot-video-object-segmentation | One-Shot Video Object Segmentation | 1611.05198 | http://arxiv.org/abs/1611.05198v4 | http://arxiv.org/pdf/1611.05198v4.pdf | https://github.com/kmaninis/OSVOS-caffe | false | false | false | tf |
https://paperswithcode.com/paper/non-turing-computations-via-malament-hogarth | Non-Turing computations via Malament-Hogarth space-times | gr-qc/0104023 | https://arxiv.org/abs/gr-qc/0104023v2 | https://arxiv.org/pdf/gr-qc/0104023v2.pdf | https://github.com/alexnieddu/Kerr-Black-Hole-Geodesics | false | false | true | none |
https://paperswithcode.com/paper/a-chi-squared-time-frequency-discriminator | A chi-squared time-frequency discriminator for gravitational wave detection | gr-qc/0405045 | https://arxiv.org/abs/gr-qc/0405045v2 | https://arxiv.org/pdf/gr-qc/0405045v2.pdf | https://github.com/gwastro/1-ogc | false | false | true | none |
https://paperswithcode.com/paper/quantum-associative-memory | Quantum Associative Memory | quant-ph/9807053 | https://arxiv.org/abs/quant-ph/9807053v1 | https://arxiv.org/pdf/quant-ph/9807053v1.pdf | https://github.com/hhy37/Liquid | false | false | true | none |
https://paperswithcode.com/paper/improved-simulation-of-stabilizer-circuits | Improved Simulation of Stabilizer Circuits | quant-ph/0406196 | https://arxiv.org/abs/quant-ph/0406196v5 | https://arxiv.org/pdf/quant-ph/0406196v5.pdf | https://github.com/hhy37/Liquid | false | false | true | none |
https://paperswithcode.com/paper/distributed-prioritized-experience-replay | Distributed Prioritized Experience Replay | 1803.00933 | http://arxiv.org/abs/1803.00933v1 | http://arxiv.org/pdf/1803.00933v1.pdf | https://github.com/neka-nat/distributed_rl | false | false | true | pytorch |
https://paperswithcode.com/paper/benchmarking-automatic-machine-learning | Benchmarking Automatic Machine Learning Frameworks | 1808.06492 | http://arxiv.org/abs/1808.06492v1 | http://arxiv.org/pdf/1808.06492v1.pdf | https://github.com/ClimbsRocks/auto_ml | false | true | false | tf |
https://paperswithcode.com/paper/a-fofe-based-local-detection-approach-for | A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection | 1611.00801 | http://arxiv.org/abs/1611.00801v1 | http://arxiv.org/pdf/1611.00801v1.pdf | https://github.com/xmb-cipher/fofe-ner | true | true | true | tf |
https://paperswithcode.com/paper/nonnegative-decomposition-of-multivariate | Nonnegative Decomposition of Multivariate Information | 1004.2515 | http://arxiv.org/abs/1004.2515v1 | http://arxiv.org/pdf/1004.2515v1.pdf | https://github.com/robince/partial-info-decomp | false | false | true | none |
https://paperswithcode.com/paper/a-tutorial-on-thompson-sampling | A Tutorial on Thompson Sampling | 1707.02038 | https://arxiv.org/abs/1707.02038v3 | https://arxiv.org/pdf/1707.02038v3.pdf | https://github.com/iosband/ts_tutorial | true | true | false | none |
https://paperswithcode.com/paper/penalizing-unfairness-in-binary | Penalizing Unfairness in Binary Classification | 1707.00044 | http://arxiv.org/abs/1707.00044v3 | http://arxiv.org/pdf/1707.00044v3.pdf | https://github.com/jjgold012/lab-project-fairness | true | true | false | none |
https://paperswithcode.com/paper/teacher-student-curriculum-learning | Teacher-Student Curriculum Learning | 1707.00183 | http://arxiv.org/abs/1707.00183v2 | http://arxiv.org/pdf/1707.00183v2.pdf | https://github.com/tambetm/TSCL | true | true | true | tf |
https://paperswithcode.com/paper/text-matching-as-image-recognition | Text Matching as Image Recognition | 1602.06359 | http://arxiv.org/abs/1602.06359v1 | http://arxiv.org/pdf/1602.06359v1.pdf | https://github.com/pl8787/MatchPyramid-TensorFlow | false | false | true | tf |
https://paperswithcode.com/paper/hyperband-a-novel-bandit-based-approach-to | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | 1603.06560 | http://arxiv.org/abs/1603.06560v4 | http://arxiv.org/pdf/1603.06560v4.pdf | https://github.com/zygmuntz/hyperband | false | false | false | none |
https://paperswithcode.com/paper/perceptual-losses-for-real-time-style | Perceptual Losses for Real-Time Style Transfer and Super-Resolution | 1603.08155 | http://arxiv.org/abs/1603.08155v1 | http://arxiv.org/pdf/1603.08155v1.pdf | https://github.com/ksivaman/super-res | false | false | true | pytorch |
https://paperswithcode.com/paper/optimal-transport-based-machine-learning-to | Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data | 2107.11192 | https://arxiv.org/abs/2107.11192v3 | https://arxiv.org/pdf/2107.11192v3.pdf | https://github.com/yen-nguyen-thi-thanh/wtot_coclust_match | true | true | false | pytorch |
https://paperswithcode.com/paper/mushroomrl-simplifying-reinforcement-learning | MushroomRL: Simplifying Reinforcement Learning Research | 2001.01102 | https://arxiv.org/abs/2001.01102v2 | https://arxiv.org/pdf/2001.01102v2.pdf | https://github.com/AIRLab-POLIMI/mushroom-rl | true | true | false | tf |
https://paperswithcode.com/paper/how-sgd-selects-the-global-minima-in-over | How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective | null | http://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective | http://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective.pdf | https://github.com/leiwu1990/sgd.stability | true | true | false | pytorch |
https://paperswithcode.com/paper/extending-text-to-speech-synthesis-with | Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging | 2107.05550 | https://arxiv.org/abs/2107.05550v1 | https://arxiv.org/pdf/2107.05550v1.pdf | https://github.com/BME-SmartLab/txt2ult | true | true | true | tf |
https://paperswithcode.com/paper/simulaqron-a-simulator-for-developing-quantum | SimulaQron - A simulator for developing quantum internet software | 1712.08032 | http://arxiv.org/abs/1712.08032v2 | http://arxiv.org/pdf/1712.08032v2.pdf | https://github.com/SoftwareQuTech/CQC-Python | false | false | true | none |
https://paperswithcode.com/paper/dgm-a-deep-learning-algorithm-for-solving | DGM: A deep learning algorithm for solving partial differential equations | 1708.07469 | http://arxiv.org/abs/1708.07469v5 | http://arxiv.org/pdf/1708.07469v5.pdf | https://github.com/alialaradi/DeepGalerkinMethod | false | false | true | tf |
https://paperswithcode.com/paper/a-spatiotemporal-volumetric-interpolation | A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image | 2002.12680 | https://arxiv.org/abs/2002.12680v2 | https://arxiv.org/pdf/2002.12680v2.pdf | https://github.com/guoyu-niubility/SVIN | true | true | false | pytorch |
https://paperswithcode.com/paper/metapruning-meta-learning-for-automatic | MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning | 1903.10258 | https://arxiv.org/abs/1903.10258v3 | https://arxiv.org/pdf/1903.10258v3.pdf | https://github.com/liuzechun/MetaPruning | true | true | true | pytorch |
https://paperswithcode.com/paper/variational-adversarial-active-learning | Variational Adversarial Active Learning | 1904.00370 | https://arxiv.org/abs/1904.00370v3 | https://arxiv.org/pdf/1904.00370v3.pdf | https://github.com/sinhasam/vaal | true | true | true | pytorch |
https://paperswithcode.com/paper/learning-higher-order-logic-programs | Learning higher-order logic programs | 1907.10953 | https://arxiv.org/abs/1907.10953v1 | https://arxiv.org/pdf/1907.10953v1.pdf | https://github.com/metagol/metagol | true | true | false | none |
https://paperswithcode.com/paper/pde-net-learning-pdes-from-data | PDE-Net: Learning PDEs from Data | 1710.09668 | http://arxiv.org/abs/1710.09668v2 | http://arxiv.org/pdf/1710.09668v2.pdf | https://github.com/agrundner24/pde-net-in-tf | false | false | true | tf |
https://paperswithcode.com/paper/x-lxmert-paint-caption-and-answer-questions | X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers | 2009.11278 | https://arxiv.org/abs/2009.11278v1 | https://arxiv.org/pdf/2009.11278v1.pdf | https://github.com/allenai/x-lxmert | true | true | false | pytorch |
https://paperswithcode.com/paper/infinitygan-towards-infinite-resolution-image | InfinityGAN: Towards Infinite-Pixel Image Synthesis | 2104.03963 | https://arxiv.org/abs/2104.03963v4 | https://arxiv.org/pdf/2104.03963v4.pdf | https://github.com/hubert0527/infinityGAN | true | false | false | pytorch |
https://paperswithcode.com/paper/exploring-data-aggregation-in-policy-learning | Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.pdf | https://github.com/autonomousvision/data_aggregation | true | true | false | none |
https://paperswithcode.com/paper/graph-structured-prediction-energy-networks | Graph Structured Prediction Energy Networks | 1910.14670 | https://arxiv.org/abs/1910.14670v2 | https://arxiv.org/pdf/1910.14670v2.pdf | https://github.com/cgraber/GSPEN | true | true | false | pytorch |
https://paperswithcode.com/paper/image-to-image-translation-with-conditional | Image-to-Image Translation with Conditional Adversarial Networks | 1611.07004 | http://arxiv.org/abs/1611.07004v3 | http://arxiv.org/pdf/1611.07004v3.pdf | https://github.com/sidneykingsley/fyp | false | false | true | tf |
https://paperswithcode.com/paper/minibatch-processing-in-spiking-neural | Minibatch Processing in Spiking Neural Networks | 1909.02549 | https://arxiv.org/abs/1909.02549v1 | https://arxiv.org/pdf/1909.02549v1.pdf | https://github.com/djsaunde/snn-minibatch | true | true | false | pytorch |
https://paperswithcode.com/paper/stochastic-chebyshev-gradient-descent-for | Stochastic Chebyshev Gradient Descent for Spectral Optimization | 1802.06355 | http://arxiv.org/abs/1802.06355v3 | http://arxiv.org/pdf/1802.06355v3.pdf | https://github.com/EiffL/SpectralFlow | false | false | true | tf |
https://paperswithcode.com/paper/learned-image-downscaling-for-upscaling-using | Learned Image Downscaling for Upscaling using Content Adaptive Resampler | 1907.12904 | https://arxiv.org/abs/1907.12904v2 | https://arxiv.org/pdf/1907.12904v2.pdf | https://github.com/twice154/ofa-for-super-resolution | false | false | 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/daxiapazi/faster-rcnn | false | false | true | tf |
https://paperswithcode.com/paper/every-positive-integer-is-a-sum-of-three | Every positive integer is a sum of three palindromes | 1602.06208 | http://arxiv.org/abs/1602.06208v2 | http://arxiv.org/pdf/1602.06208v2.pdf | https://github.com/TroyLaurin/PalindromeSum | false | false | true | none |
https://paperswithcode.com/paper/reducing-the-training-time-of-neural-networks | Reducing the Training Time of Neural Networks by Partitioning | 1511.02954 | http://arxiv.org/abs/1511.02954v2 | http://arxiv.org/pdf/1511.02954v2.pdf | https://github.com/agongt408/vbranch | false | false | true | tf |
https://paperswithcode.com/paper/net2net-accelerating-learning-via-knowledge | Net2Net: Accelerating Learning via Knowledge Transfer | 1511.05641 | http://arxiv.org/abs/1511.05641v4 | http://arxiv.org/pdf/1511.05641v4.pdf | https://github.com/agongt408/vbranch | false | false | true | tf |
https://paperswithcode.com/paper/simple-non-perturbative-resummation-schemes | Simple non-perturbative resummation schemes beyond mean-field: case study for scalar $φ^4$ theory in 1+1 dimensions | 1901.05483 | http://arxiv.org/abs/1901.05483v1 | http://arxiv.org/pdf/1901.05483v1.pdf | https://github.com/paro8929/Resummation | true | true | true | none |
https://paperswithcode.com/paper/the-maven-dependency-graph-a-temporal-graph | The Maven Dependency Graph: a Temporal Graph-based Representation of Maven Central | 1901.05392 | http://arxiv.org/abs/1901.05392v1 | http://arxiv.org/pdf/1901.05392v1.pdf | https://github.com/tdegueul/sonar-dataset | false | false | true | none |
https://paperswithcode.com/paper/adversarial-training-methods-for-network | Adversarial Training Methods for Network Embedding | 1908.11514 | https://arxiv.org/abs/1908.11514v1 | https://arxiv.org/pdf/1908.11514v1.pdf | https://github.com/wonniu/AdvT4NE_WWW2019 | true | true | false | tf |
https://paperswithcode.com/paper/central-server-free-federated-learning-over | Central Server Free Federated Learning over Single-sided Trust Social Networks | 1910.04956 | https://arxiv.org/abs/1910.04956v2 | https://arxiv.org/pdf/1910.04956v2.pdf | https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/standalone/decentralized | true | false | false | pytorch |
https://paperswithcode.com/paper/expert-load-matters-operating-networks-at-1 | Expert load matters: operating networks at high accuracy and low manual effort | 2308.05035 | https://arxiv.org/abs/2308.05035v2 | https://arxiv.org/pdf/2308.05035v2.pdf | https://github.com/salusanga/aucoc_loss | false | false | true | pytorch |
https://paperswithcode.com/paper/accurate-large-minibatch-sgd-training | Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | 1706.02677 | http://arxiv.org/abs/1706.02677v2 | http://arxiv.org/pdf/1706.02677v2.pdf | https://github.com/darkreapyre/HaaS-dev | false | false | true | tf |
https://paperswithcode.com/paper/fiber-cnn-expanding-mask-r-cnn-to-improve | FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis | 2006.04552 | https://arxiv.org/abs/2006.04552v2 | https://arxiv.org/pdf/2006.04552v2.pdf | https://github.com/maxfrei750/synthPIC4Python | true | true | true | none |
https://paperswithcode.com/paper/knee-point-identification-based-on-trade-off | Knee Point Identification Based on Trade-Off Utility | 2005.11600 | https://arxiv.org/abs/2005.11600v1 | https://arxiv.org/pdf/2005.11600v1.pdf | https://github.com/COLA-Laboratory/kpi | true | true | false | none |
https://paperswithcode.com/paper/matching-networks-for-one-shot-learning | Matching Networks for One Shot Learning | 1606.04080 | http://arxiv.org/abs/1606.04080v2 | http://arxiv.org/pdf/1606.04080v2.pdf | https://github.com/fujenchu/matchingNet | false | false | true | pytorch |
https://paperswithcode.com/paper/probabilistic-fasttext-for-multi-sense-word | Probabilistic FastText for Multi-Sense Word Embeddings | 1806.02901 | http://arxiv.org/abs/1806.02901v1 | http://arxiv.org/pdf/1806.02901v1.pdf | https://github.com/benathi/multisense-prob-fasttext | true | true | true | none |
https://paperswithcode.com/paper/self-supervised-visual-planning-with-temporal | Self-Supervised Visual Planning with Temporal Skip Connections | 1710.05268 | http://arxiv.org/abs/1710.05268v1 | http://arxiv.org/pdf/1710.05268v1.pdf | https://github.com/CompVis/image2video-synthesis-using-cINNs | false | false | true | pytorch |
https://paperswithcode.com/paper/the-hitchhikers-guide-to-lda | The Hitchhiker's Guide to LDA | 1908.03142 | https://arxiv.org/abs/1908.03142v2 | https://arxiv.org/pdf/1908.03142v2.pdf | https://github.com/MachineIntellect/GibbsLDA_plus | false | false | true | none |
https://paperswithcode.com/paper/shufflenet-v2-practical-guidelines-for | ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design | 1807.11164 | http://arxiv.org/abs/1807.11164v1 | http://arxiv.org/pdf/1807.11164v1.pdf | https://github.com/savageyusuff/MobilePose-Pi | false | false | true | pytorch |
https://paperswithcode.com/paper/unite-unified-translation-evaluation | UniTE: Unified Translation Evaluation | 2204.13346 | https://arxiv.org/abs/2204.13346v1 | https://arxiv.org/pdf/2204.13346v1.pdf | https://github.com/wanyu2018umac/UniTE | true | false | false | pytorch |
https://paperswithcode.com/paper/generative-adversarial-networks | Generative Adversarial Networks | 1406.2661 | https://arxiv.org/abs/1406.2661v1 | https://arxiv.org/pdf/1406.2661v1.pdf | https://github.com/etjoa003/medical_imaging | false | false | true | none |
https://paperswithcode.com/paper/efficient-nonparametric-statistical-inference | Efficient nonparametric statistical inference on population feature importance using Shapley values | 2006.09481 | https://arxiv.org/abs/2006.09481v1 | https://arxiv.org/pdf/2006.09481v1.pdf | https://github.com/bdwilliamson/spvim_supplementary | false | false | true | none |
https://paperswithcode.com/paper/searching-for-mobilenetv3 | Searching for MobileNetV3 | 1905.02244 | https://arxiv.org/abs/1905.02244v5 | https://arxiv.org/pdf/1905.02244v5.pdf | https://github.com/rwightman/efficientnet-jax | false | false | true | jax |
https://paperswithcode.com/paper/wiring-up-vision-minimizing-supervised | Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream | null | https://openreview.net/forum?id=5i4vRgoZauw | https://openreview.net/pdf?id=5i4vRgoZauw | https://github.com/franzigeiger/training_reductions | false | false | false | pytorch |
https://paperswithcode.com/paper/coha-ntt-a-configurable-hardware-accelerator | CoHA-NTT: A Configurable Hardware Accelerator for NTT-based Polynomial Multiplication | null | https://eprint.iacr.org/2021/1527 | https://eprint.iacr.org/2021/1527.pdf | https://github.com/kemalderya/pqc-param-ntt | false | true | false | none |
https://paperswithcode.com/paper/physics-informed-neural-networks-for-non | Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics | 2004.04026 | https://arxiv.org/abs/2004.04026v2 | https://arxiv.org/pdf/2004.04026v2.pdf | https://github.com/jbesty/PINN_system_identification | false | false | true | tf |
https://paperswithcode.com/paper/distilling-model-knowledge | Distilling Model Knowledge | 1510.02437 | http://arxiv.org/abs/1510.02437v1 | http://arxiv.org/pdf/1510.02437v1.pdf | https://github.com/gpapamak/distilling_model_knowledge | false | false | true | none |
https://paperswithcode.com/paper/effective-obstruction-to-lifting-tate-classes | Effective obstruction to lifting Tate classes from positive characteristic | 2003.11037 | https://arxiv.org/abs/2003.11037v3 | https://arxiv.org/pdf/2003.11037v3.pdf | https://github.com/edgarcosta/crystalline_obstruction | true | true | true | 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/StillKeepTry/Transformer-PyTorch | false | false | true | pytorch |
https://paperswithcode.com/paper/on-the-importance-of-capturing-a-sufficient | On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs | 2101.11164 | https://arxiv.org/abs/2101.11164v1 | https://arxiv.org/pdf/2101.11164v1.pdf | https://github.com/AdamByerly/micro-pcb-analysis | true | false | false | tf |
https://paperswithcode.com/paper/dropout-as-a-bayesian-approximation | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning | 1506.02142 | http://arxiv.org/abs/1506.02142v6 | http://arxiv.org/pdf/1506.02142v6.pdf | https://github.com/cdebeunne/uncertainties_CNN | false | false | true | pytorch |
https://paperswithcode.com/paper/gaussianprocessesjl-a-nonparametric-bayes | GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language | 1812.09064 | https://arxiv.org/abs/1812.09064v2 | https://arxiv.org/pdf/1812.09064v2.pdf | https://github.com/UnofficialJuliaMirrorSnapshots/GaussianProcesses.jl-891a1506-143c-57d2-908e-e1f8e92e6de9 | false | false | true | none |
https://paperswithcode.com/paper/tractable-higher-order-under-approximating-ae | Tractable higher-order under-approximating AE extensions for non-linear systems | 2101.11536 | https://arxiv.org/abs/2101.11536v1 | https://arxiv.org/pdf/2101.11536v1.pdf | https://github.com/cosynus-lix/RINO | true | false | false | none |
https://paperswithcode.com/paper/lightweight-probabilistic-deep-networks | Lightweight Probabilistic Deep Networks | 1805.11327 | http://arxiv.org/abs/1805.11327v1 | http://arxiv.org/pdf/1805.11327v1.pdf | https://github.com/cdebeunne/uncertainties_CNN | false | false | true | pytorch |
https://paperswithcode.com/paper/reducing-complexity-and-unidentifiability | Reducing complexity and unidentifiability when modelling human atrial cells | 2001.10954 | https://arxiv.org/abs/2001.10954v1 | https://arxiv.org/pdf/2001.10954v1.pdf | https://github.com/charleshouston/ion-channel-ABC | true | true | false | none |
https://paperswithcode.com/paper/model-agnostic-meta-learning-for-fast | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | 1703.03400 | http://arxiv.org/abs/1703.03400v3 | http://arxiv.org/pdf/1703.03400v3.pdf | https://github.com/MoritzTaylor/maml-rl-tf2 | false | false | true | tf |
https://paperswithcode.com/paper/robust-person-re-identification-by-modelling | Robust Person Re-Identification by Modelling Feature Uncertainty | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.pdf | https://github.com/TianyuanYu/DistributionNet | true | true | false | tf |
https://paperswithcode.com/paper/hierarchical-encoding-of-sequential-data-with | Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.pdf | https://github.com/intellhave/HESSL | true | true | false | none |
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