{"id":"W2941442828","doi":"10.48550/arxiv.1904.10939","title":"Horseshoe Regularization for Machine Learning in Complex and Deep Models","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"","keywords":"Horseshoe (symbol); Regularization (linguistics); Computer science; Artificial intelligence; Machine learning; Gaussian; Computation; Bayesian probability; Multivariate statistics; Algorithm","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":[0.005044787,0.001129732,0.001322233,0.001552509,0.0006470328,0.002193161,0.001425639,0.002558863,0.003354139],"category_scores_gemma":[0.0176376,0.0008340966,0.001336524,0.001359939,0.00309533,0.003811995,0.002944281,0.004212391,0.001036359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001345261,"about_ca_system_score_gemma":0.001047808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001848429,"about_ca_topic_score_gemma":0.001874049,"domain_scores_codex":[0.9981274,0.001073238,0.00008270063,0.0002405692,0.0003812393,0.00009486413],"domain_scores_gemma":[0.9947318,0.003634443,0.0004099957,0.0006271471,0.0004177067,0.000178784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001827583,0.00001561675,0.0003979472,0.000139605,0.00004904925,0.00005914647,0.0001079168,0.1055736,0.0006370373,0.8683462,0.0038607,0.02079505],"study_design_scores_gemma":[0.000003485656,0.000009111198,0.0001106876,0.00003364847,0.000005714358,0.00002327729,0.00001100672,0.4251292,0.0002049694,0.5707837,0.003673168,0.00001195567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002919787,0.001530407,0.99207,0.000885624,0.0000670614,0.00001084851,0.00007625253,0.0001339439,0.002306084],"genre_scores_gemma":[0.3521951,0.01091935,0.619449,0.001202559,0.001170827,0.0003897201,0.0006444628,0.0007801965,0.01324882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005044787,"threshold_uncertainty_score":0.02667969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07327616003506653,"score_gpt":0.1896622800032247,"score_spread":0.1163861199681582,"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."}}