{"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":"/task/tabular-classification/papers/ran/1","list_of":"/task/tabular-classification","task":"tabular-classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_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'","n_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)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","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 task or check it against the task'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.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,12],"of":12,"counts":{"archive_papers_tagged":48,"with_a_code_link":29,"where_syntology_ran_a_sample":12,"not_listed_spam_title":0,"listed":48,"listed_where_code_ran":12,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":9,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":9,"listed_every_run_a_failure_of_syntologys_instrument":3,"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 the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/tabular-classification/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/jolt-joint-probabilistic-predictions-on","slug":"jolt-joint-probabilistic-predictions-on","title":"JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs","date":"2025-02-17","arxiv_id":"2502.11877","repositories_listed":2,"syntology":{"n":19,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/jolt-joint-probabilistic-predictions-on#ran","syntology_url":"https://syntology.ai/paper/2502.11877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.11877"}},"official":{"repos":["cambridge-mlg/jolt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/bayesian-concept-bottleneck-models-with-llm","slug":"bayesian-concept-bottleneck-models-with-llm","title":"Bayesian Concept Bottleneck Models with LLM Priors","date":"2024-10-21","arxiv_id":"2410.15555","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-concept-bottleneck-models-with-llm#ran","syntology_url":"https://syntology.ai/paper/2410.15555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15555"}},"official":{"repos":["jjfeng/bc-llm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pmlbmini-a-tabular-classification-benchmark","slug":"pmlbmini-a-tabular-classification-benchmark","title":"PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications","date":"2024-09-03","arxiv_id":"2409.01635","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pmlbmini-a-tabular-classification-benchmark#ran","syntology_url":"https://syntology.ai/paper/2409.01635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01635"}},"official":{"repos":["ricardoknauer/tabmini"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tunetables-context-optimization-for-scalable","slug":"tunetables-context-optimization-for-scalable","title":"TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks","date":"2024-02-17","arxiv_id":"2402.11137","repositories_listed":2,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/tunetables-context-optimization-for-scalable#ran","syntology_url":"https://syntology.ai/paper/2402.11137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11137"}},"official":{"repos":["penfever/tabpfn-pt","penfever/tunetables"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/mothernet-a-foundational-hypernetwork-for","slug":"mothernet-a-foundational-hypernetwork-for","title":"MotherNet: Fast Training and Inference via Hyper-Network Transformers","date":"2023-12-14","arxiv_id":"2312.08598","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mothernet-a-foundational-hypernetwork-for#ran","syntology_url":"https://syntology.ai/paper/2312.08598","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08598"}},"official":{"repos":["microsoft/ticl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gate-gated-additive-tree-ensemble-for-tabular","slug":"gate-gated-additive-tree-ensemble-for-tabular","title":"GANDALF: Gated Adaptive Network for Deep Automated Learning of Features","date":"2022-07-18","arxiv_id":"2207.08548","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gate-gated-additive-tree-ensemble-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2207.08548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08548"}},"official":{"repos":["manujosephv/GATE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/meta-learning-a-real-time-tabular-automl","slug":"meta-learning-a-real-time-tabular-automl","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","date":"2022-07-05","arxiv_id":"2207.01848","repositories_listed":7,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-learning-a-real-time-tabular-automl#ran","syntology_url":"https://syntology.ai/paper/2207.01848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01848"}},"official":null}},{"url":"/paper/scarf-self-supervised-contrastive-learning","slug":"scarf-self-supervised-contrastive-learning","title":"SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption","date":"2021-06-29","arxiv_id":"2106.15147","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/scarf-self-supervised-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2106.15147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.15147"}},"official":null}},{"url":"/paper/revisiting-deep-learning-models-for-tabular","slug":"revisiting-deep-learning-models-for-tabular","title":"Revisiting Deep Learning Models for Tabular Data","date":"2021-06-22","arxiv_id":"2106.11959","repositories_listed":11,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":15,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":14,"n_pointer_only":1,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 1 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/revisiting-deep-learning-models-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2106.11959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11959"}},"official":{"repos":["yandex-research/tabular-dl-revisiting-models","Yura52/tabular-dl-revisiting-models"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/tabtransformer-tabular-data-modeling-using","slug":"tabtransformer-tabular-data-modeling-using","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","date":"2020-12-11","arxiv_id":"2012.06678","repositories_listed":12,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tabtransformer-tabular-data-modeling-using#ran","syntology_url":"https://syntology.ai/paper/2012.06678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.06678"}},"official":{"repos":["lucidrains/tab-transformer-pytorch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-explainable-ai-which-algorithmic","slug":"evaluating-explainable-ai-which-algorithmic","title":"Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?","date":"2020-05-04","arxiv_id":"2005.01831","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/evaluating-explainable-ai-which-algorithmic#ran","syntology_url":"https://syntology.ai/paper/2005.01831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01831"}},"official":{"repos":["peterbhase/InterpretableNLP-ACL2020"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-imputation-and-stochastic","slug":"generative-imputation-and-stochastic","title":"Generative Imputation and Stochastic Prediction","date":"2019-05-22","arxiv_id":"1905.09340","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generative-imputation-and-stochastic#ran","syntology_url":"https://syntology.ai/paper/1905.09340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.09340"}},"official":{"repos":["mkachuee/GenerativeImputationStochasticPrediction"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"edf6af1801494efb09deca5054b2969958fc087e8badff1fbb0ebb44c2d787ca","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}