{"id":"W3091221243","doi":"","title":"Semi-supervised Sequential Generative Models","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"MNIST database; Computer science; Estimator; Latent variable; Generative model; Generative grammar; Machine learning; Artificial intelligence; Trajectory; Forcing (mathematics); Variance (accounting); Variable (mathematics); Deep learning; Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003257584,0.0002740982,0.0003248208,0.00009911216,0.0001644119,0.0002840159,0.001090884,0.000106731,0.0001529564],"category_scores_gemma":[0.0001771968,0.000261502,0.0001293366,0.000949108,0.0001291999,0.0008140116,0.0003085817,0.0002944097,0.0001893683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000896528,"about_ca_system_score_gemma":0.0001431297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003073765,"about_ca_topic_score_gemma":0.0002185804,"domain_scores_codex":[0.9975247,0.000258744,0.0005879382,0.0007696978,0.0003508806,0.0005080508],"domain_scores_gemma":[0.9989495,0.0001521041,0.00009876578,0.0004050765,0.0001648683,0.0002296555],"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":[0.00003159426,0.00005135535,0.000008186119,0.00000487432,0.00001333807,0.00003209271,0.003920312,0.8264562,0.008275614,0.1015527,0.0002681874,0.05938552],"study_design_scores_gemma":[0.00004073197,0.00008585105,0.000002577278,0.00001434658,0.000004302084,0.000001559163,0.0003493084,0.8884271,0.05070469,0.05973781,0.0003571258,0.000274621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003241477,0.0001208696,0.9871388,0.006927011,0.00042139,0.0002999617,0.000008136414,0.0001318743,0.001710453],"genre_scores_gemma":[0.9621336,0.00004676253,0.03462091,0.002676903,0.0004413199,0.00002972323,0.000006912974,0.00001459016,0.00002929114],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9588921,"threshold_uncertainty_score":0.9999837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09722466294154446,"score_gpt":0.2841124223971038,"score_spread":0.1868877594555593,"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."}}