{"id":"W3035389942","doi":"","title":"Online Bayesian Moment Matching based SAT Solver Heuristics","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristics; Computer science; Matching (statistics); Solver; Bayesian probability; Moment (physics); Algorithm; Artificial intelligence; Mathematics; Statistics; Programming language; Physics","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":[0.00164494,0.001193902,0.001972318,0.002141959,0.001110414,0.001976595,0.003403783,0.002474541,0.0409678],"category_scores_gemma":[0.009790679,0.001300015,0.001511303,0.002729013,0.0009001168,0.003813382,0.002596759,0.002500285,0.00368375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001866405,"about_ca_system_score_gemma":0.003559054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006311927,"about_ca_topic_score_gemma":0.01385073,"domain_scores_codex":[0.9978281,0.0008085073,0.00009773446,0.0004274194,0.0005019244,0.0003362712],"domain_scores_gemma":[0.994253,0.00413897,0.0002816755,0.0005749995,0.0005451171,0.0002061918],"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":[0.001307922,0.0006212635,0.0016625,0.0002663729,0.0001676894,0.0001859203,0.0001541204,0.5493709,0.003357562,0.08844645,0.02878533,0.3256739],"study_design_scores_gemma":[0.0001189084,0.00004992243,0.0001546061,0.00001305053,0.0000265453,0.00002854759,0.00002174049,0.9662243,0.0007598647,0.03106429,0.001526113,0.00001217595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03126151,0.0004469572,0.937618,0.001138758,0.0002590303,0.0002672171,0.0008712048,0.004269343,0.02386814],"genre_scores_gemma":[0.4888677,0.0002158222,0.4946057,0.0007458105,0.0001796198,0.0003760356,0.00195884,0.0008262509,0.01222431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0409678,"threshold_uncertainty_score":0.1370509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04184371776120862,"score_gpt":0.2904388463723157,"score_spread":0.248595128611107,"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."}}