{"id":"W3037553603","doi":"","title":"Stochastic Neural Network with Kronecker Flow.","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Computer science; Generalization; Kronecker product; Kronecker delta; Artificial neural network; Inference; Scalability; Artificial intelligence; Noise (video); Machine learning; Mathematical optimization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002421632,0.001007476,0.0008157515,0.0005860011,0.0003652756,0.001027778,0.001345132,0.001318717,0.002782409],"category_scores_gemma":[0.006998084,0.0005202549,0.000558183,0.0006344066,0.001456327,0.002248109,0.001605069,0.002470272,0.0006775958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042362,"about_ca_system_score_gemma":0.001220762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003222705,"about_ca_topic_score_gemma":0.004502496,"domain_scores_codex":[0.9993305,0.0003382181,0.00003088922,0.0001053336,0.0001530679,0.00004199535],"domain_scores_gemma":[0.9981178,0.001153674,0.0002010437,0.0002432675,0.0001969644,0.000087269],"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":[0.00008353577,0.00003488568,0.0005734497,0.00009956143,0.00005369052,0.00006690169,0.00005290906,0.7192059,0.001202593,0.2356449,0.002532622,0.04044907],"study_design_scores_gemma":[0.000003554136,0.00001043125,0.00004357097,0.000007209715,0.000003279067,0.00001327899,0.000002290935,0.9517919,0.0001853935,0.04735104,0.0005842484,0.000003703973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006877278,0.0007204541,0.9886649,0.0003790212,0.00009441248,0.00003877427,0.0001171474,0.0002112932,0.002896638],"genre_scores_gemma":[0.6335885,0.00153508,0.3490652,0.0004890595,0.0002242077,0.0002908595,0.0006416664,0.000225398,0.01394012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003222705,"threshold_uncertainty_score":0.01280695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02416605865756617,"score_gpt":0.1636484482311515,"score_spread":0.1394823895735853,"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."}}