{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002709325,0.001220923,0.001230978,0.0007589463,0.0006637704,0.001265952,0.002168828,0.001538611,0.003278255],"category_scores_gemma":[0.01213629,0.0008834478,0.0009267658,0.0006819905,0.002329669,0.002674275,0.00277976,0.004509924,0.0006611138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001761773,"about_ca_system_score_gemma":0.001872375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005161356,"about_ca_topic_score_gemma":0.00599899,"domain_scores_codex":[0.9986921,0.0005089151,0.00004662455,0.0002763787,0.0003491477,0.0001267464],"domain_scores_gemma":[0.9957438,0.003059883,0.0002658136,0.0004776359,0.0003095838,0.000143287],"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":[0.00008319427,0.00002156621,0.0004367674,0.0000382757,0.00002706962,0.00005607013,0.00004855642,0.9161379,0.001052874,0.06124874,0.001116254,0.01973269],"study_design_scores_gemma":[0.000003533985,0.000004610078,0.00002984436,0.000005283515,0.000001761367,0.000007867558,0.000002012581,0.9820734,0.0003085485,0.01730024,0.0002584168,0.000004406584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01262471,0.0001615838,0.9838864,0.0003878003,0.00004438818,0.00002967696,0.0001414512,0.000520811,0.002203047],"genre_scores_gemma":[0.7288518,0.0004001719,0.263021,0.0005102659,0.0001084709,0.0002154998,0.0007977294,0.0004338305,0.005661298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005161356,"threshold_uncertainty_score":0.01432848,"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."}}