{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002850419,0.0001856303,0.0002103576,0.0001241564,0.0003413544,0.0001202879,0.001299859,0.00009285587,0.00001434009],"category_scores_gemma":[0.0002404494,0.0001836042,0.000103753,0.0004854319,0.00004248481,0.0004055536,0.0006620462,0.0003362517,0.00001168768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000605099,"about_ca_system_score_gemma":0.00003943067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001870074,"about_ca_topic_score_gemma":0.000008240719,"domain_scores_codex":[0.9985379,0.00004165396,0.0002544886,0.0005795109,0.000127063,0.0004593687],"domain_scores_gemma":[0.9988139,0.0003130721,0.00009248713,0.0006397901,0.00006309665,0.00007762774],"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":[0.00002488168,0.00003248491,0.1514943,0.0000308017,0.00005029749,0.00001229606,0.0002005711,0.768051,0.00005351225,0.05623223,0.000468679,0.02334888],"study_design_scores_gemma":[0.0004216406,0.00002837704,0.01899683,0.00003739431,0.00001675496,0.000002713001,0.000009416282,0.9772463,0.00002474591,0.002579073,0.0004602747,0.0001764995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03579893,0.0001206648,0.9596273,0.001513943,0.001754173,0.0002511534,4.233375e-7,0.0002878437,0.0006455756],"genre_scores_gemma":[0.9830364,0.000001256379,0.01492772,0.0007330111,0.0003394253,0.00004417704,0.000003653846,0.00001769815,0.0008966658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9472374,"threshold_uncertainty_score":0.7487164,"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."}}