{"id":"W592244745","doi":"10.48550/arxiv.1506.02216","title":"A Recurrent Latent Variable Model for Sequential Data","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":705,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Autoencoder; Latent variable; Recurrent neural network; Computer science; Artificial intelligence; Latent variable model; Hidden variable theory; State variable; State (computer science); Machine learning; Artificial neural network; Speech recognition; Algorithm","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":[0.0006682271,0.0003343525,0.0003857863,0.0001252361,0.0001686064,0.0002348325,0.003812258,0.0002472562,0.00001288389],"category_scores_gemma":[0.00007917761,0.0003640512,0.0001495413,0.000294226,0.00006472262,0.0007175454,0.006393957,0.0003448394,0.00002445781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171724,"about_ca_system_score_gemma":0.0007509603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001109706,"about_ca_topic_score_gemma":0.00004544403,"domain_scores_codex":[0.9974772,0.0001350348,0.0002273965,0.001610265,0.0001131448,0.0004369688],"domain_scores_gemma":[0.996487,0.00007176235,0.0002448988,0.002569385,0.0003962533,0.0002306915],"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":[0.00004054179,0.00007914835,0.00001305757,0.00003574421,0.000110876,0.00001756632,0.00008521372,0.9467477,0.00002037331,0.03750171,0.0145126,0.0008354946],"study_design_scores_gemma":[0.0004493503,0.00003874092,0.000002524337,0.0000492127,0.0001210374,0.000001118156,0.000007001147,0.9217642,0.00002607166,0.07453573,0.002624243,0.0003807975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006048143,0.0001335284,0.9959058,0.0001352829,0.00144815,0.0005301887,0.0003501929,0.0001394728,0.0007525707],"genre_scores_gemma":[0.8647785,0.0001465227,0.1314772,0.0001075752,0.0003602723,0.000004057675,0.0003581023,0.00002841706,0.002739289],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8644286,"threshold_uncertainty_score":0.9998811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2753690116820647,"score_gpt":0.2356292871584844,"score_spread":0.03973972452358032,"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."}}