{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677241,0.0009170744,0.0007175062,0.000339686,0.0001928909,0.0005192547,0.001285959,0.0007976001,0.003115116],"category_scores_gemma":[0.001994329,0.0004960681,0.0006217654,0.00040527,0.0003668094,0.0008849545,0.0006885562,0.001389736,0.0008394459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005281239,"about_ca_system_score_gemma":0.0005206473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003614428,"about_ca_topic_score_gemma":0.008153414,"domain_scores_codex":[0.9997255,0.00007199762,0.00001422312,0.00009512962,0.00005372108,0.00003940459],"domain_scores_gemma":[0.9994584,0.0002978656,0.00003468976,0.00007517682,0.000105371,0.00002855326],"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":[0.0001793267,0.00008305997,0.0007211116,0.00009582535,0.00007945337,0.0001784495,0.00006277498,0.8076188,0.014805,0.01242155,0.004502783,0.1592519],"study_design_scores_gemma":[0.000001980554,0.00001062075,0.00003946979,0.000002219563,0.000002485982,0.00001706133,0.00000137913,0.9973807,0.001036181,0.001222699,0.0002825624,0.000002518003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0102132,0.0004120177,0.9862324,0.0001162319,0.00007097822,0.00004266313,0.0002038551,0.0008611711,0.001847413],"genre_scores_gemma":[0.6777416,0.0004347877,0.3103922,0.0003381268,0.0001071738,0.0002618474,0.001443014,0.0002644288,0.009016854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003614428,"threshold_uncertainty_score":0.0104211,"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."}}