{"id":"W3094065920","doi":"","title":"Implicit Variational Inference: the Parameter and the Predictor Space.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Inference; Space (punctuation); Artificial neural network; Posterior probability; Bayesian inference; Parameter space; Distribution (mathematics); Computer science; Artificial intelligence; Mathematics; Bayesian probability; Applied mathematics; Machine learning; Mathematical optimization; Statistics; Mathematical analysis","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":[0.0003407448,0.0002437848,0.0002682078,0.00004505373,0.0003268513,0.0003538157,0.00151408,0.0001407417,0.00003179573],"category_scores_gemma":[0.0002176287,0.0001525372,0.0001607314,0.0003156828,0.0003241174,0.0002389886,0.002383748,0.0005317729,0.00002351379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004181785,"about_ca_system_score_gemma":0.0001597509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001515699,"about_ca_topic_score_gemma":0.00001781415,"domain_scores_codex":[0.9983906,0.0003904645,0.0001533504,0.0007277103,0.0001110984,0.0002268248],"domain_scores_gemma":[0.997719,0.001052218,0.0001986925,0.0008101462,0.0001177515,0.0001021598],"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":[0.00005857837,0.00002016954,0.0009742122,0.00001426822,0.0002331082,0.00002195805,0.0007864179,0.2001047,0.00001481612,0.7954907,0.001680019,0.0006010685],"study_design_scores_gemma":[0.0004360971,0.00001799703,0.004020371,0.00001384989,0.00007430252,0.000001487262,0.00003127411,0.8127369,0.00001604485,0.1815259,0.0009630346,0.0001627399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006696502,0.00008595946,0.9845189,0.006567138,0.0004488992,0.0003957278,0.00001929086,0.00007102427,0.001196496],"genre_scores_gemma":[0.995918,0.0001895154,0.0025951,0.0006711167,0.0002545002,0.000003312075,0.000005687466,0.000008630724,0.0003540973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9892215,"threshold_uncertainty_score":0.6220286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05306780710344853,"score_gpt":0.1859541692401949,"score_spread":0.1328863621367464,"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."}}