{"id":"W2421157170","doi":"10.48550/arxiv.1506.03877","title":"Bidirectional Helmholtz Machines","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Inference; Generative model; Computer science; Generative grammar; Bhattacharyya distance; Fiducial inference; Frequentist inference; Artificial intelligence; Helmholtz free energy; Algorithm; Machine learning; Bayesian inference; Bayesian probability","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":[0.001382738,0.001025773,0.001107104,0.00102432,0.0008777287,0.001480446,0.001789817,0.002108362,0.008287286],"category_scores_gemma":[0.00655653,0.0009083075,0.001406935,0.0009596833,0.002306122,0.002644316,0.003601847,0.002425376,0.002653823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315505,"about_ca_system_score_gemma":0.0009627513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500671,"about_ca_topic_score_gemma":0.002691232,"domain_scores_codex":[0.9989666,0.0003835998,0.00003991449,0.0002773206,0.0002155996,0.0001170843],"domain_scores_gemma":[0.9984599,0.0009734455,0.000112663,0.0002701988,0.0001029791,0.00008072459],"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":[0.00007364398,0.00003070687,0.0004130952,0.0001078327,0.00004707132,0.00008798207,0.0001586139,0.4263126,0.00302299,0.5090197,0.003433418,0.05729236],"study_design_scores_gemma":[0.000008691344,0.00001017515,0.0000695954,0.00001993782,0.000006531885,0.00003811306,0.00001364879,0.658101,0.001222413,0.3379042,0.002591917,0.00001370558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007659998,0.0003187416,0.983026,0.000411238,0.00004544786,0.00003665177,0.0002142188,0.0007214856,0.007566205],"genre_scores_gemma":[0.5523722,0.001056626,0.4138967,0.0009194146,0.0002458629,0.0004381307,0.001025426,0.000867027,0.02917865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008287286,"threshold_uncertainty_score":0.02772373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08125603442208122,"score_gpt":0.1908260872491965,"score_spread":0.1095700528271153,"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."}}