{"id":"W3103356223","doi":"10.18653/v1/2020.emnlp-main.101","title":"Learning VAE-LDA Models with Rounded Reparameterization Trick","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Artificial intelligence; Generative grammar; Benchmark (surveying); Topic model; Dirichlet distribution; Generative model; Machine learning; Mathematics","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":[0.003977786,0.001322132,0.002066175,0.001393793,0.0008109632,0.002186133,0.002865153,0.002064891,0.003954111],"category_scores_gemma":[0.01324415,0.001050173,0.002202794,0.001614102,0.001300002,0.00444593,0.003230331,0.004902567,0.003261463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008239135,"about_ca_system_score_gemma":0.0009325422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683024,"about_ca_topic_score_gemma":0.003005057,"domain_scores_codex":[0.9976479,0.001339482,0.0001101465,0.0004953779,0.0002609928,0.0001460666],"domain_scores_gemma":[0.9959942,0.002670783,0.0002093294,0.0007102321,0.0002971062,0.000118416],"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":[0.0002849474,0.0002372937,0.001997564,0.0002965206,0.0003067832,0.0001966892,0.0005645787,0.4766966,0.005396469,0.1165761,0.01453213,0.3829144],"study_design_scores_gemma":[0.00001725695,0.000024596,0.0001002304,0.00001666517,0.00001225518,0.00004715355,0.00002333794,0.9487323,0.0006553841,0.04868757,0.00166521,0.00001802806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008427133,0.0006016762,0.9886602,0.0003112059,0.00005456027,0.00005690309,0.0001423584,0.0007184747,0.001027428],"genre_scores_gemma":[0.3861386,0.001381857,0.5988458,0.000893851,0.0004351208,0.001151989,0.00242228,0.0006284705,0.008101927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003977786,"threshold_uncertainty_score":0.0210368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0449443518106457,"score_gpt":0.2241349230300437,"score_spread":0.179190571219398,"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."}}