{"id":"W4293118255","doi":"10.1007/978-3-031-01588-5_9","title":"Deep Generative Models","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on artificial intelligence and machine learning","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Generative grammar; Computer science; Graph; Theoretical computer science; Key (lock); Artificial intelligence; Generative model; Process (computing); Data science; Programming language","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":[0.0003123551,0.0007875721,0.0006254087,0.0006007433,0.000408427,0.001626887,0.000789598,0.001050997,0.03073484],"category_scores_gemma":[0.00125876,0.0007118511,0.0007218798,0.0006313132,0.0008993624,0.0015165,0.001301109,0.001794468,0.01183566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007528092,"about_ca_system_score_gemma":0.000526707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751335,"about_ca_topic_score_gemma":0.002600605,"domain_scores_codex":[0.9998335,0.00003485967,0.000005179081,0.00004331684,0.00006762301,0.00001563131],"domain_scores_gemma":[0.9997341,0.0001212526,0.00001112551,0.00007265946,0.00004010115,0.00002089324],"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":[0.00002076242,0.00002256417,0.0001900101,0.00009138889,0.00003698503,0.00006566494,0.00009908559,0.07632919,0.002181948,0.7304847,0.04212336,0.1483544],"study_design_scores_gemma":[0.000007380214,0.00001030289,0.0001623415,0.00004491731,0.00001682489,0.0001062946,0.00001888614,0.1626984,0.001655787,0.7254264,0.1098314,0.00002102862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004054467,0.00378769,0.8123691,0.001538117,0.0006763536,0.00002884104,0.0007725266,0.001828101,0.1749448],"genre_scores_gemma":[0.2660112,0.007535615,0.1904392,0.001044612,0.0009581172,0.000172922,0.003532744,0.002413512,0.5278921],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03073484,"threshold_uncertainty_score":0.1028183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2066218497401466,"score_gpt":0.3742896252103348,"score_spread":0.1676677754701882,"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."}}