{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005146963,0.0002322061,0.0002280154,0.0003140358,0.000406146,0.0005967401,0.0008635344,0.00009846989,0.0007858344],"category_scores_gemma":[0.001162338,0.0002393111,0.0001511352,0.0003026874,0.00005097192,0.0007894666,0.0001911228,0.0006246603,0.0001728279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001348872,"about_ca_system_score_gemma":0.0003671514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001422142,"about_ca_topic_score_gemma":0.000004567565,"domain_scores_codex":[0.9975854,0.0001828253,0.0004782182,0.0006992819,0.0007005974,0.0003537231],"domain_scores_gemma":[0.997857,0.0005879533,0.000430584,0.000352106,0.0006646382,0.0001076718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004203055,0.00003557541,0.03720502,0.00001522365,0.000104639,0.000004442143,0.001395103,0.4522868,0.006686909,0.4976999,0.00008048894,0.004443946],"study_design_scores_gemma":[0.0007411995,0.0002105007,0.002629823,0.00005904821,0.000009633781,0.00001321754,0.0003013427,0.9885542,0.000300386,0.004696483,0.002195743,0.000288358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286041,0.000005486434,0.8471735,0.002593915,0.00151577,0.0006431268,0.00001578074,0.0004199983,0.01902845],"genre_scores_gemma":[0.9421259,0.000008361312,0.04568903,0.0001351158,0.0002559896,0.00007336694,0.0002233366,0.00002396064,0.01146496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8135219,"threshold_uncertainty_score":0.9758825,"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."}}