{"url":"/task/sparse-learning","name":"Sparse Learning","slug":"sparse-learning","description_markdown":null,"categories":[{"name":"Methodology","url":"/area/methodology"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":185,"papers_with_code":52,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":3,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/sparse-learning-on-imagenet","slug":"sparse-learning-on-imagenet","dataset":"ImageNet","dataset_url":"/dataset/imagenet","rows_in_archive":9,"metrics":["Top-1 Accuracy"],"first_row_in_archive_order":{"model":"Resnet-50: 80% Sparse","paper_title":"Rigging the Lottery: Making All Tickets Winners","paper_url":"/paper/rigging-the-lottery-making-all-tickets-1","paper_date":"2019-11-25","arxiv_id":"1911.11134","code_links":[{"title":"google-research/rigl","url":"https://github.com/google-research/rigl"},{"title":"verbose-avocado/rigl-torch","url":"https://github.com/verbose-avocado/rigl-torch"},{"title":"nollied/rigl-torch","url":"https://github.com/nollied/rigl-torch"},{"title":"hyeon95y/sparselinear","url":"https://github.com/hyeon95y/sparselinear"},{"title":"Shiweiliuiiiiiii/In-Time-Over-Parameterization","url":"https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization"},{"title":"vita-group/granet","url":"https://github.com/vita-group/granet"},{"title":"Shiweiliuiiiiiii/GraNet","url":"https://github.com/Shiweiliuiiiiiii/GraNet"},{"title":"varun19299/rigl-reproducibility","url":"https://github.com/varun19299/rigl-reproducibility"},{"title":"calgaryml/condensed-sparsity","url":"https://github.com/calgaryml/condensed-sparsity"},{"title":"stevenboys/moon","url":"https://github.com/stevenboys/moon"},{"title":"stevenboys/agent","url":"https://github.com/stevenboys/agent"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/sparse-learning-on-cinic-10-1","slug":"sparse-learning-on-cinic-10-1","dataset":"CINIC-10","dataset_url":"/dataset/cinic-10","rows_in_archive":1,"metrics":["Sparsity"],"first_row_in_archive_order":{"model":"Resnet18","paper_title":"Adaptive Neural Connections for Sparsity Learning","paper_url":"/paper/adaptive-neural-connections-for-sparsity","paper_date":"2020-03-05","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/sparse-learning-on-imagenet32-1","slug":"sparse-learning-on-imagenet32-1","dataset":"ImageNet32","dataset_url":"/dataset/imagenet-32","rows_in_archive":1,"metrics":["Sparsity"],"first_row_in_archive_order":{"model":"Resnet18","paper_title":"Adaptive Neural Connections for Sparsity Learning","paper_url":"/paper/adaptive-neural-connections-for-sparsity","paper_date":"2020-03-05","arxiv_id":null,"code_links":[],"syntology":null}}],"datasets":[{"url":"/dataset/imagenet","name":"ImageNet","full_name":"","num_papers_in_archive":15430},{"url":"/dataset/cinic-10","name":"CINIC-10","full_name":"CINIC-10","num_papers_in_archive":197},{"url":"/dataset/imagenet-32","name":"ImageNet-32","full_name":"","num_papers_in_archive":112}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":52,"tagged_in_all":185,"items":[{"url":"/paper/variational-dropout-sparsifies-deep-neural","title":"Variational Dropout Sparsifies Deep Neural Networks","date":"2017-01-19","arxiv_id":"1701.05369","repositories_listed":15,"syntology":{"n":3,"n_ran":1,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/rigging-the-lottery-making-all-tickets-1","title":"Rigging the Lottery: Making All Tickets Winners","date":"2019-11-25","arxiv_id":"1911.11134","repositories_listed":11,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/the-state-of-sparsity-in-deep-neural-networks","title":"The State of Sparsity in Deep Neural Networks","date":"2019-02-25","arxiv_id":"1902.09574","repositories_listed":6,"syntology":null},{"url":"/paper/do-we-actually-need-dense-over","title":"Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training","date":"2021-02-04","arxiv_id":"2102.02887","repositories_listed":4,"syntology":null},{"url":"/paper/a-general-iterative-shrinkage-and","title":"A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems","date":"2013-03-18","arxiv_id":"1303.4434","repositories_listed":4,"syntology":{"n":12,"n_ran":1,"n_unverified":11,"n_pointer_only":0}},{"url":"/paper/controlled-sparsity-via-constrained","title":"Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints","date":"2022-08-08","arxiv_id":"2208.04425","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/abess-a-fast-best-subset-selection-library-in","title":"abess: A Fast Best Subset Selection Library in Python and R","date":"2021-10-19","arxiv_id":"2110.09697","repositories_listed":2,"syntology":null},{"url":"/paper/sparse-training-via-boosting-pruning","title":"Sparse Training via Boosting Pruning Plasticity with Neuroregeneration","date":"2021-06-19","arxiv_id":"2106.10404","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/sparse-regression-at-scale-branch-and-bound","title":"Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization","date":"2020-04-13","arxiv_id":"2004.06152","repositories_listed":2,"syntology":{"n":18,"n_ran":0,"n_unverified":18,"n_pointer_only":0}},{"url":"/paper/sparse-networks-from-scratch-faster-training","title":"Sparse Networks from Scratch: Faster Training without Losing Performance","date":"2019-07-10","arxiv_id":"1907.04840","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/scalable-training-of-artificial-neural","title":"Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science","date":"2017-07-15","arxiv_id":"1707.04780","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/feature-selection-a-data-perspective","title":"Feature Selection: A Data Perspective","date":"2016-01-29","arxiv_id":"1601.07996","repositories_listed":2,"syntology":null},{"url":"/paper/scalable-subset-selection-in-linear-mixed","title":"Scalable Subset Selection in Linear Mixed Models","date":"2025-06-25","arxiv_id":"2506.20425","repositories_listed":1,"syntology":null},{"url":"/paper/myesl-sparse-learning-in-molecular-evolution","title":"MyESL: Sparse learning in molecular evolution and phylogenetic analysis","date":"2025-01-09","arxiv_id":"2501.04941","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-rare-word-accuracy-in-direct","title":"Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach","date":"2024-09-13","arxiv_id":"2409.09009","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-iterative-hard-thresholding-for","title":"Probabilistic Iterative Hard Thresholding for Sparse Learning","date":"2024-09-02","arxiv_id":"2409.01413","repositories_listed":1,"syntology":null},{"url":"/paper/ssprop-energy-efficient-training-for","title":"ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back Propagation","date":"2024-08-22","arxiv_id":"2408.12561","repositories_listed":1,"syntology":null},{"url":"/paper/2408-02279","title":"DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting","date":"2024-08-05","arxiv_id":"2408.02279","repositories_listed":1,"syntology":null},{"url":"/paper/sltrain-a-sparse-plus-low-rank-approach-for","title":"SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining","date":"2024-06-04","arxiv_id":"2406.02214","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/classp-a-biologically-inspired-approach-to-1","title":"CLASSP: a Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity Promotion","date":"2024-04-29","arxiv_id":"2405.09637","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-diversification-from-block","title":"Block Sparse Bayesian Learning: A Diversified Scheme","date":"2024-02-07","arxiv_id":"2402.04646","repositories_listed":1,"syntology":null},{"url":"/paper/building-explainable-graph-neural-network-by","title":"Building explainable graph neural network by sparse learning for the drug-protein binding prediction","date":"2023-08-27","arxiv_id":"2309.12906","repositories_listed":1,"syntology":null},{"url":"/paper/hypersparse-neural-networks-shifting","title":"HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization","date":"2023-08-14","arxiv_id":"2308.07163","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/resource-constrained-model-compression-via","title":"Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks","date":"2023-08-09","arxiv_id":"2308.04672","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-super-resolve-blurry-images-with","title":"Learning to Super-Resolve Blurry Images with Events","date":"2023-02-27","arxiv_id":"2302.13766","repositories_listed":1,"syntology":null},{"url":"/paper/video-text-retrieval-by-supervised-multi","title":"Video-Text Retrieval by Supervised Sparse Multi-Grained Learning","date":"2023-02-19","arxiv_id":"2302.09473","repositories_listed":1,"syntology":null},{"url":"/paper/federated-sparse-training-lottery-aware-model","title":"Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge","date":"2022-08-27","arxiv_id":"2208.13092","repositories_listed":1,"syntology":null},{"url":"/paper/adamix-mixture-of-adapter-for-parameter","title":"AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning","date":"2022-05-24","arxiv_id":"2205.12410","repositories_listed":1,"syntology":null},{"url":"/paper/app-anytime-progressive-pruning","title":"APP: Anytime Progressive Pruning","date":"2022-04-04","arxiv_id":"2204.01640","repositories_listed":1,"syntology":null},{"url":"/paper/l0learn-a-scalable-package-for-sparse","title":"L0Learn: A Scalable Package for Sparse Learning using L0 Regularization","date":"2022-02-10","arxiv_id":"2202.04820","repositories_listed":1,"syntology":null}],"syntology_records":10,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}