{"id":"W3132413728","doi":"10.48550/arxiv.2102.06559","title":"Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Stochastic differential equation; Estimator; Inference; Computer science; Bayesian probability; Ode; Bayesian inference; Stochastic neural network; Applied mathematics; Posterior probability; Algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Recurrent neural network; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007087211,0.000471691,0.0004381313,0.0002499838,0.0002917725,0.0006974878,0.001958168,0.000315513,0.0001087097],"category_scores_gemma":[0.00003513859,0.0004754113,0.0001891758,0.001007458,0.0001514837,0.0005877989,0.001779727,0.0009316956,0.00001314605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001086247,"about_ca_system_score_gemma":0.0003453762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008182544,"about_ca_topic_score_gemma":0.0002567535,"domain_scores_codex":[0.9975384,0.0001277057,0.0002622377,0.001346354,0.0001637357,0.0005616021],"domain_scores_gemma":[0.9976267,0.0001700743,0.0003202023,0.00130487,0.0002844114,0.000293767],"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":[0.0000212851,0.00009466575,0.0003939617,0.0000550839,0.00009059266,0.0003899851,0.0002292759,0.9176708,0.000002026862,0.07945061,0.000009923803,0.001591743],"study_design_scores_gemma":[0.0004006105,0.00008617396,0.001081087,0.0001338151,0.0001083217,0.0000172969,0.00007559486,0.9909402,0.000004515324,0.006573734,0.000003781675,0.0005748327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02651799,0.00008316171,0.9717757,0.0001084299,0.0005439853,0.0002481123,0.00000585846,0.0002600667,0.000456695],"genre_scores_gemma":[0.9959665,0.00002410447,0.003460585,0.0001051022,0.0001242506,0.000003251357,0.00004970378,0.00002706698,0.0002394562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9694485,"threshold_uncertainty_score":0.9997697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0396526418469261,"score_gpt":0.1778123489010236,"score_spread":0.1381597070540975,"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."}}