{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001693713,0.0002169013,0.0002800623,0.0002427289,0.0001016979,0.0001558566,0.0007918387,0.0002034256,0.00000838151],"category_scores_gemma":[0.00002282312,0.0002484446,0.0000669466,0.0003432118,0.00004727738,0.0004708163,0.000987699,0.0003571619,0.000005355425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007551331,"about_ca_system_score_gemma":0.0001109078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006307529,"about_ca_topic_score_gemma":0.00006312691,"domain_scores_codex":[0.9986106,0.0000616831,0.0001639677,0.000847826,0.00005697758,0.0002589687],"domain_scores_gemma":[0.999099,0.0000646376,0.0001864722,0.0004681664,0.0001044394,0.00007725917],"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":[0.00001768076,0.0000285165,0.003336347,0.0002029382,0.00001466048,0.00001796509,0.0002263076,0.548762,0.00001510516,0.4454672,0.00001340542,0.001897898],"study_design_scores_gemma":[0.0003591184,0.00003952111,0.001132418,0.00005391381,0.00001253753,0.000002183508,0.0000185032,0.7899679,0.00001067734,0.2080631,0.0001143146,0.0002258077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01436474,0.0001063747,0.9833655,0.0001400146,0.00009784879,0.0003665863,0.000008027167,0.0001031995,0.001447701],"genre_scores_gemma":[0.9875597,0.0002158851,0.01126895,0.0000585153,0.00001740112,0.000001532299,0.0000499538,0.00001339491,0.0008146023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.973195,"threshold_uncertainty_score":0.9999968,"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."}}