{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002297844,0.0008461931,0.0009166949,0.0006761064,0.0003152752,0.00100548,0.002003542,0.001411895,0.002696716],"category_scores_gemma":[0.005309721,0.0006812026,0.00101028,0.000813033,0.001357814,0.002374242,0.001294266,0.002307232,0.0005203055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079418,"about_ca_system_score_gemma":0.0007200791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00428021,"about_ca_topic_score_gemma":0.005451661,"domain_scores_codex":[0.9989045,0.0003993587,0.00004143438,0.0003941279,0.0001672579,0.00009325589],"domain_scores_gemma":[0.9977708,0.001456449,0.0002735899,0.0002674865,0.000160106,0.00007161043],"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":[0.00008196837,0.00004118606,0.001339216,0.00007297096,0.00008359525,0.0001429432,0.0001523765,0.8318586,0.002863441,0.1386348,0.001263412,0.02346553],"study_design_scores_gemma":[0.000003326877,0.00001149242,0.000098856,0.000004631085,0.000005613466,0.00001529653,0.000003987687,0.9783037,0.0002016895,0.0209707,0.0003754746,0.000005330367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01774263,0.0002882668,0.9799876,0.000425717,0.00004599754,0.00001998115,0.0002299243,0.0002383118,0.001021495],"genre_scores_gemma":[0.834803,0.0005636938,0.152177,0.0002917538,0.0001515731,0.0001871854,0.0008472074,0.0001891766,0.01078942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00428021,"threshold_uncertainty_score":0.01215231,"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."}}