{"id":"W6979234844","doi":"","title":"Stochastic Weight Sharing for Bayesian Neural Networks","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"H2020 Marie Skłodowska-Curie Actions; HORIZON EUROPE Framework Programme; European Commission","keywords":"Artificial neural network; Bayesian probability; Leverage (statistics); Inference; Gaussian process; Bayesian inference; Probabilistic logic; Bayesian network; Gaussian; Deep neural networks","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":[0.003653653,0.0009660696,0.0009806873,0.0008828234,0.0005255087,0.001269626,0.001895905,0.001104093,0.002349372],"category_scores_gemma":[0.0136214,0.0006188016,0.0006686096,0.0008362485,0.001807819,0.003389951,0.002402937,0.002452034,0.0004511137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001635861,"about_ca_system_score_gemma":0.001250585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002914273,"about_ca_topic_score_gemma":0.003238042,"domain_scores_codex":[0.9983026,0.000682672,0.00009290509,0.0002604412,0.000559018,0.000102318],"domain_scores_gemma":[0.9969657,0.001811247,0.000287763,0.0005391868,0.0002910151,0.0001051253],"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":[0.00006545527,0.00002704099,0.0003624195,0.00007309957,0.00003910802,0.00003278563,0.00005370557,0.7877356,0.001507537,0.1496463,0.001142277,0.05931458],"study_design_scores_gemma":[0.000005043553,0.00001077647,0.00005193798,0.00001009913,0.00000374432,0.00001099344,0.000003591494,0.9141641,0.0004343342,0.08484626,0.0004533303,0.000005863017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005538766,0.0002325801,0.9927707,0.000212316,0.00002188691,0.00001972056,0.00006545025,0.0001698211,0.0009686579],"genre_scores_gemma":[0.6963884,0.001024751,0.2980811,0.0003133014,0.0001743155,0.0002789045,0.0004031503,0.000189782,0.003146251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003653653,"threshold_uncertainty_score":0.01932257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019261329382488,"score_gpt":0.2774973388431143,"score_spread":0.2582360094606263,"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."}}