{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002653917,0.0001402408,0.0002020045,0.00009895403,0.0001854939,0.0001570252,0.0005059666,0.00004364763,0.000382546],"category_scores_gemma":[0.001379233,0.0001283258,0.00009748625,0.0001484791,0.00002668342,0.0002905203,0.0001045118,0.0002880353,0.00004213218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000251384,"about_ca_system_score_gemma":0.00006678039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005951398,"about_ca_topic_score_gemma":0.00000425402,"domain_scores_codex":[0.9985998,0.0001752112,0.0002849323,0.0003907412,0.0003981958,0.0001511567],"domain_scores_gemma":[0.9986132,0.0005497526,0.0002153804,0.0001260804,0.0004209901,0.00007458988],"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":[0.0002233889,0.00005892801,0.003347219,0.000007055471,0.00007907305,0.000001621713,0.0006243497,0.516586,0.01068494,0.4564403,0.0002229825,0.01172409],"study_design_scores_gemma":[0.000925581,0.000460513,0.0009175566,0.00003440223,0.000006998688,8.015925e-7,0.00005370583,0.9896938,0.001391871,0.003450238,0.002941443,0.0001231468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00293748,0.00001543587,0.9722952,0.01148221,0.0002423924,0.0001951989,0.00001443879,0.00007157895,0.01274604],"genre_scores_gemma":[0.9748161,0.00001414502,0.02348114,0.0006354458,0.0001761068,0.00002268686,0.00005872432,0.00001163776,0.000783989],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9718786,"threshold_uncertainty_score":0.5232976,"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."}}