{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/uci-machine-learning-repository/papers/ran/1","list_of":"/dataset/uci-machine-learning-repository","dataset":"UCI Machine Learning Repository","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,5],"of":5,"counts":{"papers_with_a_benchmark_row":16,"with_a_code_link":16,"where_syntology_ran_a_sample":5,"not_listed_spam_title":0,"listed":16,"listed_where_code_ran":5,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":4,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":4,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/uci-machine-learning-repository/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/block-neural-autoregressive-flow","slug":"block-neural-autoregressive-flow","title":"Block Neural Autoregressive Flow","date":"2019-04-09","arxiv_id":"1904.04676","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["nicola-decao/BNAF"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/block-neural-autoregressive-flow#ran","syntology_url":"https://syntology.ai/paper/1904.04676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04676"}}}},{"paper":"/paper/ffjord-free-form-continuous-dynamics-for","slug":"ffjord-free-form-continuous-dynamics-for","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","date":"2018-10-02","arxiv_id":"1810.01367","rows_on_this_dataset":4,"code_links":7,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":2,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ffjord-free-form-continuous-dynamics-for#ran","syntology_url":"https://syntology.ai/paper/1810.01367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.01367"}}}},{"paper":"/paper/brits-bidirectional-recurrent-imputation-for","slug":"brits-bidirectional-recurrent-imputation-for","title":"BRITS: Bidirectional Recurrent Imputation for Time Series","date":"2018-05-27","arxiv_id":"1805.10572","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["WenjieDu/PyPOTS"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/brits-bidirectional-recurrent-imputation-for#ran","syntology_url":"https://syntology.ai/paper/1805.10572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.10572"}}}},{"paper":"/paper/masked-autoregressive-flow-for-density","slug":"masked-autoregressive-flow-for-density","title":"Masked Autoregressive Flow for Density Estimation","date":"2017-05-19","arxiv_id":"1705.07057","rows_on_this_dataset":3,"code_links":21,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":28,"samples_ran":22,"samples_constructed":14,"samples_ran_checked":22,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":9,"official":{"repos":["gpapamak/maf"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/masked-autoregressive-flow-for-density#ran","syntology_url":"https://syntology.ai/paper/1705.07057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.07057"}}}},{"paper":"/paper/made-masked-autoencoder-for-distribution","slug":"made-masked-autoencoder-for-distribution","title":"MADE: Masked Autoencoder for Distribution Estimation","date":"2015-02-12","arxiv_id":"1502.03509","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":3,"samples_unverified":4,"pointer_only_for_licence":1,"official":{"repos":["mgermain/MADE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/made-masked-autoencoder-for-distribution#ran","syntology_url":"https://syntology.ai/paper/1502.03509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1502.03509"}}}}],"record_sha256":"12d266f8c5670aafc87a80aeb0d9aa45c9f6dbd8256a8d716cc7c538b5404a3d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}