{"id":"W2963408680","doi":"","title":"DVAE++: Discrete Variational Autoencoders with Overlapping Transformations","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"D-Wave Systems (Canada)","funders":"","keywords":"Latent variable; Prior probability; Transformation (genetics); Softmax function; Smoothing; Computer science; Artificial intelligence; Algorithm; Mathematics; Applied mathematics; Convolutional neural network; Statistics; Bayesian probability","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":[0.0002171691,0.0001638145,0.0001243398,0.0001501398,0.0003567387,0.0003685314,0.0005970229,0.00003878732,0.0005403825],"category_scores_gemma":[0.00007292841,0.0001328874,0.00005179869,0.0002011486,0.00008180848,0.0008465425,0.00007151841,0.0002668525,0.0001283672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005352894,"about_ca_system_score_gemma":0.0001007103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007278909,"about_ca_topic_score_gemma":0.00006438242,"domain_scores_codex":[0.9986903,0.0001030593,0.0002105417,0.0003287503,0.0004560988,0.0002112473],"domain_scores_gemma":[0.9992168,0.00008796091,0.0001276897,0.0001728912,0.0003237125,0.00007093955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008060288,0.00005816759,0.001085159,0.000003648147,0.0001576306,0.000008836001,0.001938887,0.1507157,0.001281451,0.8209771,0.000230354,0.02346241],"study_design_scores_gemma":[0.0003559931,0.0002202653,0.00178489,0.00004283572,0.0000056324,0.00001008924,0.00005086196,0.9836002,0.0002346079,0.002584863,0.01092816,0.0001816115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005310574,0.000006664967,0.9143276,0.006825495,0.0003451816,0.00008349942,0.000007702733,0.0001139453,0.07775885],"genre_scores_gemma":[0.9376459,0.00002055476,0.06067841,0.0004509002,0.0002700943,0.0000132815,0.00003461638,0.000009621408,0.0008765905],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9371149,"threshold_uncertainty_score":0.5916809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068640998251981,"score_gpt":0.2663003432703145,"score_spread":0.2456139332877947,"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."}}