{"id":"W2965465046","doi":"","title":"Learning proposals for sequential importance samplers using reinforced variational inference.","year":2019,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Computer science; Artificial intelligence; Machine learning","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":[0.008636348,0.001390237,0.002602318,0.001531719,0.0007435239,0.001884474,0.004215426,0.003100244,0.00588489],"category_scores_gemma":[0.0464287,0.00226818,0.0014494,0.001471366,0.00244632,0.004112665,0.003230775,0.0052924,0.001301125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382522,"about_ca_system_score_gemma":0.002302189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00573525,"about_ca_topic_score_gemma":0.009774229,"domain_scores_codex":[0.9977252,0.001339718,0.00009644531,0.0003423623,0.0002929058,0.0002032922],"domain_scores_gemma":[0.9722268,0.02410537,0.0006656387,0.001213555,0.001090278,0.0006983842],"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":[0.001014151,0.0003158928,0.003495142,0.0003888801,0.0003219579,0.0001655562,0.0003697323,0.6247612,0.001720521,0.214579,0.009970718,0.1428972],"study_design_scores_gemma":[0.00007654202,0.00003035922,0.00006366408,0.00002186265,0.00001792231,0.00001373771,0.000009225895,0.9544361,0.0002082754,0.04459978,0.0005144307,0.000008162379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008638158,0.0004400722,0.9893523,0.000270981,0.0001146322,0.00008178483,0.00008651745,0.0003602753,0.0006552399],"genre_scores_gemma":[0.4421158,0.0005672773,0.5477868,0.0005625978,0.0004879935,0.000790427,0.001133229,0.00051117,0.006044552],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008636348,"threshold_uncertainty_score":0.04567391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05752040479871633,"score_gpt":0.3716738520567237,"score_spread":0.3141534472580074,"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."}}