{"url":"/dataset/uci-machine-learning-repository","name":"UCI Machine Learning Repository","full_name":null,"description_markdown":"**UCI Machine Learning Repository** is a collection of over 550 datasets.","description_withheld":null,"homepage":"http://archive.ics.uci.edu/ml/datasets.php","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/endgame-analysis-of-dou-shou-qi","title":"Endgame Analysis of Dou Shou Qi","first_author":"Jan N. van Rijn","url":null},"license":null,"modalities":[],"tasks":[{"name":"Time Series Classification","url":"/task/time-series-classification","datasets_with_task":"/datasets/task/time-series-classification"},{"name":"Density Estimation","url":"/task/density-estimation","datasets_with_task":"/datasets/task/density-estimation"},{"name":"Core set discovery","url":"/task/core-set-discovery","datasets_with_task":"/datasets/task/core-set-discovery"},{"name":"Multivariate Time Series Imputation","url":"/task/multivariate-time-series-imputation","datasets_with_task":"/datasets/task/multivariate-time-series-imputation"},{"name":"Synthetic Data Generation","url":"/task/synthetic-data-generation","datasets_with_task":"/datasets/task/synthetic-data-generation"},{"name":"Gaussian Processes","url":"/task/gaussian-processes","datasets_with_task":"/datasets/task/gaussian-processes"}],"languages":[],"variants":["UCI localization data","UCI Epileptic Seizure Recognition","UCI POWER","UCI GAS","UCI HEPMASS","UCI MINIBOONE","UCI Machine Learning Repository"],"data_loaders":[{"repo":"https://github.com/WenjieDu/TSDB","url":"https://github.com/WenjieDu/TSDB","frameworks":[]}],"num_papers_in_archive":56,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/density-estimation-on-uci-power","task":"Density Estimation","dataset_variant":"UCI POWER","rows":6,"metrics":["Log-likelihood","NLL","CD","EMD","MMD-CD","MMD-EMD"],"first_row_in_archive_order":{"model":"nMDMA","paper":"/paper/marginalizable-density-models","metrics":{"Log-likelihood":"1.78"},"code_links":[{"title":"dargilboa/mdma","url":"https://github.com/dargilboa/mdma"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/density-estimation-on-uci-gas","task":"Density Estimation","dataset_variant":"UCI GAS","rows":5,"metrics":["Log-likelihood","CD","EMD","MMD-CD","MMD-EMD"],"first_row_in_archive_order":{"model":"B-NAF","paper":"/paper/block-neural-autoregressive-flow","metrics":{"Log-likelihood":"12.06"},"code_links":[{"title":"nicola-decao/BNAF","url":"https://github.com/nicola-decao/BNAF"},{"title":"metachenyiyan/BreezeForest","url":"https://github.com/metachenyiyan/BreezeForest"},{"title":"Naagar/Glow_NormalizingFlow_implimentation","url":"https://github.com/Naagar/Glow_NormalizingFlow_implimentation"},{"title":"sshish/NF","url":"https://github.com/sshish/NF"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/density-estimation-on-uci-hepmass","task":"Density Estimation","dataset_variant":"UCI HEPMASS","rows":5,"metrics":["Log-likelihood","NLL","CD","EMD","MMD-CD","MMD-EMD"],"first_row_in_archive_order":{"model":"FFJORD","paper":"/paper/ffjord-free-form-continuous-dynamics-for","metrics":{"CD":"13.8","EMD":"0.164","Log-likelihood":"-14.92","MMD-CD":"13.8","MMD-EMD":"0.158","NLL":"14.92"},"code_links":[{"title":"rtqichen/ffjord","url":"https://github.com/rtqichen/ffjord"},{"title":"francois-rozet/zuko","url":"https://github.com/francois-rozet/zuko"},{"title":"jacobjinkelly/easy-neural-ode","url":"https://github.com/jacobjinkelly/easy-neural-ode"},{"title":"BorealisAI/continuous-time-flow-process","url":"https://github.com/BorealisAI/continuous-time-flow-process"},{"title":"jackgoffinet/ffjord-lite","url":"https://github.com/jackgoffinet/ffjord-lite"},{"title":"mandubian/pytorch-neural-ode","url":"https://github.com/mandubian/pytorch-neural-ode"},{"title":"lenz3000/ffjord-path","url":"https://github.com/lenz3000/ffjord-path"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/density-estimation-on-uci-miniboone","task":"Density Estimation","dataset_variant":"UCI MINIBOONE","rows":5,"metrics":["Log-likelihood","NLL","CD","EMD","MMD-CD","MMD-EMD"],"first_row_in_archive_order":{"model":"DDE","paper":"/paper/learning-generative-models-using-denoising-1","metrics":{"Log-likelihood":"-6.94","NLL":"6.94"},"code_links":[{"title":"logchan/dde","url":"https://github.com/logchan/dde"},{"title":"siavashBigdeli/DDE","url":"https://github.com/siavashBigdeli/DDE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-imputation-on-uci","task":"Multivariate Time Series Imputation","dataset_variant":"UCI localization data","rows":5,"metrics":["MAE (10% missing)"],"first_row_in_archive_order":{"model":"BRITS","paper":"/paper/brits-bidirectional-recurrent-imputation-for","metrics":{"MAE (10% missing)":"0.219"},"code_links":[{"title":"WenjieDu/PyPOTS","url":"https://github.com/WenjieDu/PyPOTS"},{"title":"WenjieDu/SAITS","url":"https://github.com/WenjieDu/SAITS"},{"title":"caow13/BRITS","url":"https://github.com/caow13/BRITS"},{"title":"NIPS-BRITS/BRITS","url":"https://github.com/NIPS-BRITS/BRITS"},{"title":"flaviagiammarino/brits-tensorflow","url":"https://github.com/flaviagiammarino/brits-tensorflow"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/synthetic-data-generation-on-uci-epileptic-1","task":"Synthetic Data Generation","dataset_variant":"UCI Epileptic Seizure Recognition","rows":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"corGAN","paper":"/paper/cor-gan-correlation-capturing-convolutional","metrics":{"AUROC":"0.92"},"code_links":[{"title":"astorfi/cor-gan","url":"https://github.com/astorfi/cor-gan"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/core-set-discovery-on-uci-gas","task":"Core set discovery","dataset_variant":"UCI GAS","rows":1,"metrics":["F1(10-fold)"],"first_row_in_archive_order":{"model":"EvoCore","paper":"/paper/uncovering-coresets-for-classification-with","metrics":{"F1(10-fold)":"94.6"},"code_links":[{"title":"pietrobarbiero/meco","url":"https://github.com/pietrobarbiero/meco"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/gaussian-processes-on-uci-power","task":"Gaussian Processes","dataset_variant":"UCI POWER","rows":1,"metrics":["Root mean square error (RMSE)"],"first_row_in_archive_order":{"model":"ICKy, periodic","paper":"/paper/incorporating-prior-knowledge-into-neural","metrics":{"Root mean square error (RMSE)":"0.033"},"code_links":[{"title":"jzy95310/ick","url":"https://github.com/jzy95310/ick"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-classification-on-uci-epileptic","task":"Time Series Classification","dataset_variant":"UCI Epileptic Seizure Recognition","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TSEM","paper":"/paper/tsem-temporally-weighted-spatiotemporal","metrics":{"Accuracy":"0.891"},"code_links":[{"title":"a11to1n3/tsem","url":"https://github.com/a11to1n3/tsem"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/paddingflow-improving-normalizing-flows-with","title":"PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise","date":"2024-03-13","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/tsem-temporally-weighted-spatiotemporal","title":"TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series","date":"2022-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/incorporating-prior-knowledge-into-neural","title":"Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel","date":"2022-05-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/marginalizable-density-models","title":"Marginalizable Density Models","date":"2021-06-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/uncovering-coresets-for-classification-with","title":"Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms","date":"2020-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cor-gan-correlation-capturing-convolutional","title":"CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records","date":"2020-01-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-generative-models-using-denoising-1","title":"Learning Generative Models using Denoising Density Estimators","date":"2020-01-08","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/pate-gan-generating-synthetic-data-with","title":"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees","date":"2019-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/block-neural-autoregressive-flow","title":"Block Neural Autoregressive Flow","date":"2019-04-09","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ffjord-free-form-continuous-dynamics-for","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","date":"2018-10-02","rows_on_this_dataset":4,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/brits-bidirectional-recurrent-imputation-for","title":"BRITS: Bidirectional Recurrent Imputation for Time Series","date":"2018-05-27","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/estimating-missing-data-in-temporal-data","title":"Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks","date":"2017-11-23","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/imputets-time-series-missing-value-imputation","title":"imputeTS: Time Series Missing Value Imputation in R","date":"2017-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/masked-autoregressive-flow-for-density","title":"Masked Autoregressive Flow for Density Estimation","date":"2017-05-19","rows_on_this_dataset":3,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":28,"samples_ran":22,"samples_unverified":6,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/made-masked-autoencoder-for-distribution","title":"MADE: Masked Autoencoder for Distribution Estimation","date":"2015-02-12","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multiple-imputation-using-chained-equations","title":"Multiple imputation using chained equations: issues and guidance for practice","date":"2010-11-30","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":50,"samples_ran":35,"samples_unverified":15,"pointer_only_for_licence":10,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}