{"id":"W2963487749","doi":"","title":"Selecting the Best in GANs Family: a Post Selection Inference Framework","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Inference; Artificial intelligence","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":[0.005232492,0.001720952,0.00238659,0.001415923,0.0007380128,0.00158801,0.00373473,0.002770639,0.006657686],"category_scores_gemma":[0.01078755,0.001112313,0.001461567,0.001141145,0.0008753756,0.00302431,0.001726231,0.004494478,0.002125816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007091181,"about_ca_system_score_gemma":0.001296115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002648228,"about_ca_topic_score_gemma":0.005206198,"domain_scores_codex":[0.9976625,0.001230684,0.00009539955,0.0005090233,0.0003348828,0.000167518],"domain_scores_gemma":[0.995662,0.002858944,0.0001269746,0.0006433551,0.0005563761,0.0001521945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007657056,0.000520235,0.003587445,0.0002354534,0.0004776807,0.0002253502,0.0001667557,0.3770121,0.004689657,0.03978358,0.03228728,0.5402488],"study_design_scores_gemma":[0.00002082705,0.00005110909,0.0001324804,0.00001614057,0.00003110881,0.00003395708,0.00000923724,0.9845995,0.0009113948,0.01314297,0.001043245,0.000008063291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006944682,0.00055777,0.9888229,0.0004146501,0.0001182912,0.00008089608,0.0002225938,0.001475216,0.001362928],"genre_scores_gemma":[0.3477807,0.0007458091,0.6331316,0.001208289,0.0006717123,0.0004829694,0.002662591,0.001320725,0.01199567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006657686,"threshold_uncertainty_score":0.02767241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04152539822682128,"score_gpt":0.3655370502695997,"score_spread":0.3240116520427784,"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."}}