{"id":"W590442793","doi":"10.48550/arxiv.1506.03504","title":"Data Generation as Sequential Decision Making","year":2015,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Generative grammar; Imputation (statistics); Machine learning; Artificial intelligence; Construct (python library); Generative model; Iterative and incremental development; Data mining; Missing data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003723048,0.0001182967,0.0001114924,0.00008592454,0.0001554942,0.0001664505,0.001370273,0.00005891333,0.00003255873],"category_scores_gemma":[0.0001190157,0.0001238334,0.00003909058,0.0004590661,0.00003960768,0.001649562,0.0009310217,0.0000821125,0.0002312952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000780917,"about_ca_system_score_gemma":0.0001275292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004900745,"about_ca_topic_score_gemma":0.00007690063,"domain_scores_codex":[0.998805,0.000119977,0.000110957,0.0006552527,0.0001033533,0.0002053917],"domain_scores_gemma":[0.9985024,0.00005626634,0.00007601465,0.00110433,0.0001325739,0.0001284289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006252855,0.00009266222,0.0008212688,0.000002833365,0.00006192411,0.0004050625,0.0002381151,0.8306103,0.001100377,0.1071443,0.02435771,0.03510291],"study_design_scores_gemma":[0.0003149416,0.00004170611,0.00007997241,0.000009274988,0.00001587525,0.000007649612,0.00003399198,0.9849788,0.0003186094,0.008372423,0.005670096,0.0001566425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04693426,0.00003670181,0.9497926,0.0000758108,0.0006530364,0.00007628676,0.000005756497,0.00007741927,0.0023481],"genre_scores_gemma":[0.9723618,0.00001648088,0.02678367,0.0001696145,0.0002782366,1.136077e-7,0.00001545875,0.000006691971,0.0003679577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9254275,"threshold_uncertainty_score":0.5049779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2344298734713957,"score_gpt":0.2424164581063529,"score_spread":0.007986584634957161,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}