{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002051888,0.0001391634,0.0002253361,0.00006499434,0.00006104216,0.00001545167,0.0002732427,0.00009080754,0.001752839],"category_scores_gemma":[0.0002448997,0.0001338554,0.00008075888,0.000328459,0.000103565,0.00009897392,0.00007835322,0.0002207936,0.0003605428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005723071,"about_ca_system_score_gemma":0.0000489881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001560695,"about_ca_topic_score_gemma":0.00001101398,"domain_scores_codex":[0.9989915,0.0001053157,0.0001543618,0.0003970746,0.00007049972,0.0002812561],"domain_scores_gemma":[0.9985479,0.0007395124,0.00008206896,0.0004252363,0.00006095731,0.000144357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003302661,0.00005285443,0.02219343,0.00002716092,0.00002143182,0.00006264085,0.00004454823,0.00005238779,0.00007642633,0.9765864,0.0004904692,0.0003591996],"study_design_scores_gemma":[0.0004873077,0.00008286648,0.005929654,0.00002318565,0.00003114963,0.000003518745,0.00007646441,0.1007833,0.00006817562,0.891514,0.0007841637,0.0002162471],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6716221,0.000005063764,0.2977261,0.00005177172,0.0001255597,0.0001405435,0.000009386001,0.0001067061,0.03021278],"genre_scores_gemma":[0.9394274,0.0000109467,0.05668374,0.0001099703,0.00002475535,2.89476e-7,0.000002047248,0.00001489139,0.003725936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2678053,"threshold_uncertainty_score":0.9991597,"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."}}