{"id":"W2806839893","doi":"10.48550/arxiv.1806.00509","title":"Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generator (circuit theory); Encoder; Computer science; Discriminator; Frame (networking); Sequence (biology); Encoding (memory); Pattern recognition (psychology); Artificial intelligence; Algorithm; Speech recognition; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004037443,0.0002905788,0.0002626939,0.0001717928,0.0003127862,0.0003197575,0.003029231,0.0002377892,0.00004749315],"category_scores_gemma":[0.00004296014,0.0003307566,0.0001303466,0.0002901715,0.0001063116,0.0005607877,0.001859013,0.0002746719,0.00004270847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001579309,"about_ca_system_score_gemma":0.000666626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000336778,"about_ca_topic_score_gemma":0.00004237489,"domain_scores_codex":[0.9976453,0.00008796602,0.0002133668,0.001589393,0.0001166162,0.000347318],"domain_scores_gemma":[0.9971851,0.00005437059,0.0003250618,0.002046439,0.0002518753,0.000137171],"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":[0.0002304668,0.000858459,0.001387066,0.001738001,0.0005506873,0.000279661,0.0009752592,0.6990612,0.003374522,0.0713267,0.1771772,0.04304076],"study_design_scores_gemma":[0.0004525847,0.00004935858,0.0000217982,0.00009638951,0.00006635028,9.735902e-7,0.000005280656,0.9838063,0.001661216,0.004856583,0.008598333,0.0003848716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02678628,0.00005897417,0.9696015,0.0002434324,0.002012397,0.0003536687,0.0000998922,0.000192478,0.0006514567],"genre_scores_gemma":[0.9837916,0.00002014813,0.01371,0.0003950398,0.0009100908,0.000001946867,0.0006040421,0.00001942886,0.0005477346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9570053,"threshold_uncertainty_score":0.9999145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2757204558892079,"score_gpt":0.2453286448086389,"score_spread":0.03039181108056904,"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."}}