{"id":"W2788626059","doi":"10.1609/aaai.v32i1.11744","title":"Learning Predictive State Representations From Non-Uniform Sampling","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"","keywords":"Observable; Computer science; Artificial intelligence; Sampling (signal processing); Machine learning; Conditional probability distribution; Process (computing); Conditional expectation; Algorithm; Mathematics; Econometrics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002510105,0.001307931,0.001572565,0.0009363801,0.0004523049,0.001395295,0.002246807,0.001500283,0.002407876],"category_scores_gemma":[0.0138217,0.0008112194,0.001076126,0.001003542,0.001282308,0.003350962,0.001972695,0.002917411,0.0007473988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009677113,"about_ca_system_score_gemma":0.001153423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004414145,"about_ca_topic_score_gemma":0.005081553,"domain_scores_codex":[0.9987766,0.0004083747,0.00007439269,0.0003833959,0.0002372769,0.0001198958],"domain_scores_gemma":[0.9936157,0.004222085,0.0005449528,0.0009658031,0.0005237367,0.0001275867],"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":[0.0001261613,0.00007563951,0.00116348,0.00008817276,0.00004982004,0.00006173823,0.0000901187,0.904987,0.001026071,0.0243399,0.001469206,0.06652254],"study_design_scores_gemma":[0.000003080317,0.00000888067,0.00005969736,0.000004441345,0.000002865022,0.000005515254,0.000003318572,0.9932966,0.000186084,0.006332225,0.00009391793,0.000003404183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01757315,0.0001973471,0.9802364,0.0001751514,0.00003255027,0.00003799594,0.0002005266,0.0008299864,0.0007169092],"genre_scores_gemma":[0.8230485,0.0004033128,0.1706238,0.0003418081,0.0001457673,0.0003065421,0.002258684,0.0002212757,0.002650177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004414145,"threshold_uncertainty_score":0.01327485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04491740335700904,"score_gpt":0.2876444453865415,"score_spread":0.2427270420295325,"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."}}