{"id":"W2562337126","doi":"","title":"A Scaled Gradient Descent Method for Unconstrained Optimization Problems With A Priori Estimation of the Minimum Value","year":2017,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"A priori and a posteriori; Gradient descent; Mathematical optimization; Mathematics; Value (mathematics); Descent (aeronautics); Computer science; Applied mathematics; Statistics; Artificial intelligence; Engineering; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006759829,0.0002602528,0.0003044819,0.0001121206,0.0001497041,0.00004639077,0.0003486956,0.0001970579,0.0002516519],"category_scores_gemma":[0.00001457066,0.0002180955,0.0001323301,0.0001687602,0.00004642862,0.0001199769,0.00002264589,0.0001505068,4.2258e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001257925,"about_ca_system_score_gemma":0.00008036552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004941427,"about_ca_topic_score_gemma":0.0002480291,"domain_scores_codex":[0.9991767,0.00004746833,0.0001815348,0.0002444406,0.0001663955,0.0001833927],"domain_scores_gemma":[0.9991242,0.00003905223,0.0002805411,0.0003577437,0.0001563877,0.0000421149],"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":[0.0001896623,0.00003637952,0.00002804429,0.0004771417,0.0002109186,0.000004376642,0.0007089582,0.8313127,0.001271666,0.0006200309,0.0001901896,0.16495],"study_design_scores_gemma":[0.001316201,0.0001584118,0.0001564717,0.002132576,0.0006012667,0.000006967249,0.0005720353,0.9579101,0.02807793,0.0002851312,0.008309131,0.0004737543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003726483,0.0000680852,0.8794382,0.00002539052,0.000399973,0.001797539,0.00003529822,0.0002787076,0.1142304],"genre_scores_gemma":[0.238363,0.00007142752,0.5972564,0.0000225782,0.0001085623,0.00003632155,0.0005221301,0.0002467367,0.1633728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2821818,"threshold_uncertainty_score":0.8893678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266251478952934,"score_gpt":0.2195353591680864,"score_spread":0.206872844378557,"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."}}