{"id":"W3034917202","doi":"","title":"On Variational Learning of Controllable Representations for Text without Supervision","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Autoencoder; Computer science; Unsupervised learning; Artificial intelligence; Decoding methods; Space (punctuation); Encoding (memory); Simplex; Sequence (biology); Latent variable; Machine learning; Deep learning; Algorithm; 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":[0.001294628,0.001033039,0.0008319074,0.000525652,0.0004226251,0.0008271868,0.001350951,0.001345798,0.00293584],"category_scores_gemma":[0.006149759,0.0006922206,0.0008547649,0.0005309148,0.001850767,0.001881495,0.001614147,0.002330346,0.000625035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049071,"about_ca_system_score_gemma":0.0009089898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003880935,"about_ca_topic_score_gemma":0.005297554,"domain_scores_codex":[0.9994417,0.0002218932,0.00002324182,0.0001645668,0.00009239154,0.00005630384],"domain_scores_gemma":[0.9973797,0.001941322,0.000166953,0.0002706003,0.0001501531,0.00009140465],"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":[0.0000778118,0.00005203352,0.0005090853,0.00007923382,0.00004195422,0.00007936451,0.0001404063,0.8959621,0.00352985,0.04881119,0.002009115,0.04870781],"study_design_scores_gemma":[0.000004543129,0.00001088382,0.00003229776,0.000004388796,0.000001762859,0.000007258224,0.000004233414,0.9857428,0.0003904701,0.01356022,0.0002376332,0.00000352163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01270205,0.0001649233,0.9848968,0.0002453169,0.00002784096,0.00003566375,0.0001022035,0.0003924257,0.001432888],"genre_scores_gemma":[0.7362354,0.0004712056,0.2475663,0.0005146468,0.00016652,0.0003912109,0.0009389121,0.0006334956,0.0130823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003880935,"threshold_uncertainty_score":0.009821296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04101088640936427,"score_gpt":0.3026475110613343,"score_spread":0.26163662465197,"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."}}