{"id":"W1798702550","doi":"10.48550/arxiv.1502.05700","title":"Scalable Bayesian Optimization Using Deep Neural Networks","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":440,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; National Energy Research Scientific Computing Center; Canadian Institute for Advanced Research; FAS Division of Science, Harvard University; U.S. Department of Energy; Harvard University; National Science Foundation","keywords":"Computer science; Bayesian optimization; Hyperparameter; Artificial intelligence; Artificial neural network; Convolutional neural network; Machine learning; Gaussian process; Global Positioning System; Deep learning; Benchmark (surveying); Optimization problem; Scalability; Surrogate model; Scale (ratio); Gaussian; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003506914,0.0002464372,0.0002308694,0.0002077316,0.0001980157,0.0002762661,0.001398994,0.0002790945,0.00002368492],"category_scores_gemma":[0.00005175125,0.0002907668,0.00009093125,0.0006251398,0.00005628445,0.0005996948,0.001374149,0.0005941226,0.00001512797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288014,"about_ca_system_score_gemma":0.0001188642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002455984,"about_ca_topic_score_gemma":0.00002274833,"domain_scores_codex":[0.9982349,0.0002505184,0.0001811435,0.0009339853,0.00009110034,0.0003083178],"domain_scores_gemma":[0.9980234,0.00004395564,0.0002963197,0.001231024,0.0002052836,0.0002000464],"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":[0.000008501574,0.00002702648,0.002431117,0.00001484564,0.00001555441,0.0000311177,0.0000474427,0.9851941,0.000001004292,0.01081468,0.0001097092,0.001304829],"study_design_scores_gemma":[0.000215684,0.00001843086,0.0002102396,0.00002310328,0.00003794356,0.000005740778,0.00001752841,0.996922,0.000001118542,0.002138042,0.0001102724,0.0002998871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00499872,0.00007977828,0.9922126,0.0001019448,0.0006255783,0.0001620863,0.000003665801,0.0003161442,0.001499464],"genre_scores_gemma":[0.9682637,0.00003609151,0.03102631,0.00006817008,0.0001291644,3.979094e-7,0.0001296089,0.00001853732,0.0003279731],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.963265,"threshold_uncertainty_score":0.9999545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07416914068091068,"score_gpt":0.2071218247056708,"score_spread":0.1329526840247601,"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."}}