{"id":"W4312852115","doi":"10.1561/9781638280538","title":"Convex Optimization for Machine Learning","year":2022,"lang":"en","type":"book","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Computer science; Regular polygon; Artificial intelligence; Mathematical optimization; Mathematics; Geometry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003687615,0.0002194141,0.0002720181,0.0001573523,0.0003516496,0.0001555478,0.0008103134,0.0001104595,0.002740273],"category_scores_gemma":[0.00007412787,0.0002102289,0.0001423066,0.00009496819,0.00001588504,0.0001143607,0.0004033717,0.0006644404,0.00003326903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009590523,"about_ca_system_score_gemma":0.0002114231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001687756,"about_ca_topic_score_gemma":0.000001107165,"domain_scores_codex":[0.9986934,0.0000708529,0.0002192253,0.0005005794,0.0002918446,0.0002241013],"domain_scores_gemma":[0.9991035,0.0002204604,0.0001956868,0.0003527939,0.00006338843,0.00006420881],"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":[0.000007279588,0.00003087639,0.00001430757,0.0001198855,0.00007047985,0.00001329008,0.000238572,0.6164068,7.613601e-7,0.1764763,0.09949614,0.1071254],"study_design_scores_gemma":[0.0001342156,0.0000894113,2.355611e-7,0.000005824253,0.00000564129,0.000005244573,0.000001174647,0.5245621,7.837163e-7,0.0009414594,0.4741099,0.0001439851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[4.474641e-8,0.0002088691,0.6813834,0.0003918285,0.0004510919,0.0001868779,0.000007258396,0.0003999324,0.3169707],"genre_scores_gemma":[0.000004987251,0.00003297098,0.1924798,0.0003338692,0.0001994289,0.00004489266,0.0004311136,0.00003390173,0.806439],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4894684,"threshold_uncertainty_score":0.9981713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101386096750196,"score_gpt":0.232322193364539,"score_spread":0.2213083323970371,"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."}}