{"id":"W2940988007","doi":"10.1109/iccv.2019.00770","title":"Improved Conditional VRNNs for Video Prediction","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Latent variable; Computer science; Probabilistic logic; Artificial intelligence; Generative model; Machine learning; Generative grammar; Latent variable model; Hierarchy; Sequence (biology); Conditional probability; Variable (mathematics); Sign (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001347617,0.0007718852,0.0008233595,0.000780482,0.0002845877,0.0005902937,0.001729563,0.0009183007,0.003606732],"category_scores_gemma":[0.004058025,0.0004960728,0.0007561654,0.0006810429,0.0006275714,0.001322677,0.0009824239,0.001909665,0.0009330163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143911,"about_ca_system_score_gemma":0.0007221971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0117394,"about_ca_topic_score_gemma":0.01455776,"domain_scores_codex":[0.9994563,0.0001531899,0.00002401602,0.0001773411,0.0001262216,0.00006288744],"domain_scores_gemma":[0.9986871,0.000769408,0.0001165863,0.0001837297,0.0001886818,0.00005451792],"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":[0.0001084737,0.00003814132,0.0006763829,0.00004370373,0.0000419549,0.00005835013,0.00004349686,0.8838405,0.002558071,0.01419374,0.00305954,0.0953377],"study_design_scores_gemma":[0.000001749494,0.000003924412,0.00004818777,0.000003628729,0.000002204624,0.000007487517,0.000001324411,0.9963195,0.0003313096,0.003073062,0.0002052349,0.000002276226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02274214,0.0007130263,0.9718373,0.0003256502,0.00007644976,0.00003302296,0.0003725285,0.001646882,0.002252901],"genre_scores_gemma":[0.774696,0.0006268003,0.2126287,0.0003911589,0.0001391249,0.0001047322,0.001835185,0.0004378495,0.009140518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0117394,"threshold_uncertainty_score":0.02334213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896619644317105,"score_gpt":0.24357964108587,"score_spread":0.224613444642699,"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."}}