{"id":"W2964155212","doi":"","title":"Adversarial Distillation of Bayesian Neural Network Posteriors","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bayesian probability; Artificial neural network; Posterior probability; Distillation; Artificial intelligence; Machine learning; Markov chain Monte Carlo; Langevin dynamics; Adversarial system; Variance (accounting); Mathematics; Statistics; Chemistry","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":[0.0004458288,0.0002294531,0.000245964,0.0001968475,0.0002387426,0.0001652471,0.001185471,0.00009245607,0.0005702786],"category_scores_gemma":[0.000456282,0.0002240784,0.0001013362,0.0003105943,0.0001441577,0.000510224,0.0004496709,0.0005513144,0.00006426634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006924903,"about_ca_system_score_gemma":0.0000689614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001532156,"about_ca_topic_score_gemma":0.00003033295,"domain_scores_codex":[0.9978943,0.0002662508,0.0004317072,0.0004828276,0.000612732,0.0003121969],"domain_scores_gemma":[0.9986021,0.0001757105,0.0004328924,0.0003345578,0.0003670631,0.00008769269],"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":[0.0004113988,0.00007051875,0.06346118,0.00001337608,0.0001174884,0.00002776714,0.001199988,0.2821175,0.001544078,0.4961655,0.0001258724,0.1547453],"study_design_scores_gemma":[0.0005425754,0.0003533124,0.00693403,0.00006467471,0.000007278146,0.00001350321,0.00002552517,0.9847213,0.0001219443,0.004709959,0.002292322,0.0002135969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03027844,0.00001638136,0.9192001,0.002798729,0.003395187,0.0001909927,0.0000057771,0.0002853326,0.04382901],"genre_scores_gemma":[0.9782375,0.000005823467,0.02006908,0.0002196106,0.0009657462,0.00000602682,0.00003178615,0.00001994136,0.0004445299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.947959,"threshold_uncertainty_score":0.913765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322181708132971,"score_gpt":0.2942357252324058,"score_spread":0.2710139081510761,"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."}}