{"id":"W2944335570","doi":"10.48550/arxiv.1905.04866","title":"Hierarchical Importance Weighted Autoencoders","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Upper and lower bounds; Convergence (economics); Variance (accounting); Inference; Maximization; Computer science; Algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","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":[0.003218775,0.0009759431,0.001464576,0.0008732379,0.0004558573,0.001115885,0.00241621,0.001436562,0.002825398],"category_scores_gemma":[0.01118533,0.0008845486,0.0009641016,0.0009103527,0.001051509,0.001928305,0.002074329,0.002601882,0.0006025948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131585,"about_ca_system_score_gemma":0.001367425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004273371,"about_ca_topic_score_gemma":0.008830407,"domain_scores_codex":[0.9982493,0.0007156593,0.00007450268,0.0004013662,0.000399896,0.0001593236],"domain_scores_gemma":[0.9962739,0.002211728,0.0002659239,0.0006513537,0.0004770967,0.0001200853],"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":[0.0001839264,0.0001256964,0.002835671,0.000195171,0.0002688098,0.00009892577,0.0002116724,0.6150625,0.005120395,0.1964156,0.004764222,0.1747173],"study_design_scores_gemma":[0.00001073866,0.00001669701,0.0002030448,0.000009659519,0.0000150536,0.00001356007,0.000005623075,0.9735286,0.0004439204,0.02512341,0.0006234891,0.000006153422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008615194,0.000282894,0.9893896,0.0001372281,0.00004004741,0.00003285099,0.00007901683,0.0001935009,0.00122963],"genre_scores_gemma":[0.5220878,0.0005575429,0.4687174,0.0004107576,0.0002381263,0.0002468736,0.0006616255,0.0001870586,0.006892817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004273371,"threshold_uncertainty_score":0.01702267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.109344872311339,"score_gpt":0.2449483154517652,"score_spread":0.1356034431404261,"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."}}