{"id":"W3125376726","doi":"10.48550/arxiv.2106.13024","title":"Symmetric Wasserstein Autoencoders","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002759807,0.0003914596,0.0004590447,0.0003922357,0.0002134741,0.0004306257,0.002063198,0.0003222206,0.00007477061],"category_scores_gemma":[0.00008664474,0.0004528291,0.0003954328,0.001620367,0.00009082841,0.0006333185,0.002674054,0.0005995654,0.00007234954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002457468,"about_ca_system_score_gemma":0.0003667437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002601278,"about_ca_topic_score_gemma":0.00007175088,"domain_scores_codex":[0.9973627,0.0003141836,0.0002259924,0.001483621,0.0001357023,0.0004778378],"domain_scores_gemma":[0.9975692,0.000150266,0.0002383443,0.001559454,0.0002593771,0.0002233315],"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":[0.00000617124,0.00008634322,0.0003472804,0.00003853934,0.0001779391,0.0006365695,0.0001808277,0.9588231,0.00005581378,0.03685398,0.0009152156,0.001878196],"study_design_scores_gemma":[0.0002615564,0.00003074176,0.0004799243,0.00006953668,0.00007513099,0.000004001423,0.0001672095,0.9919817,0.0004341567,0.005273576,0.0006810088,0.0005414868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02051423,0.000292151,0.9709594,0.0002344164,0.001367288,0.0001939594,0.000005488576,0.0002376897,0.00619535],"genre_scores_gemma":[0.978776,0.0003133224,0.01857388,0.0001847512,0.0001406079,9.467975e-7,0.00001673367,0.0000207121,0.001973022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9582618,"threshold_uncertainty_score":0.9997923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06214300876735093,"score_gpt":0.1739100751952475,"score_spread":0.1117670664278965,"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."}}