{"id":"W2963586744","doi":"","title":"Identifying and attacking the saddle point problem in high-dimensional non-convex optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":494,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Maxima and minima; Saddle point; Gradient descent; Saddle; Artificial neural network; Mathematical optimization; Computer science; Convex optimization; Convex function; Mathematics; Applied mathematics; Regular polygon; Algorithm; Artificial intelligence; Mathematical analysis; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002137162,0.0009893855,0.0009811593,0.0006093235,0.0005365573,0.0007838633,0.0009013574,0.001323953,0.001470937],"category_scores_gemma":[0.007485571,0.0005727753,0.0007480572,0.0004237827,0.002115991,0.001222029,0.00190055,0.002294962,0.000460157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004612797,"about_ca_system_score_gemma":0.00101096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001921069,"about_ca_topic_score_gemma":0.00224453,"domain_scores_codex":[0.9995568,0.0002190916,0.00002164391,0.00004691964,0.0001256631,0.00002970126],"domain_scores_gemma":[0.9970822,0.002275928,0.0002201499,0.0001821664,0.0001685471,0.00007099653],"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":[0.00006670298,0.00003338387,0.0007204732,0.0001995305,0.00008212027,0.0001586237,0.0002049711,0.8331126,0.004168903,0.1244844,0.002282892,0.03448544],"study_design_scores_gemma":[0.000007663787,0.00002350714,0.00007056957,0.00001377953,0.000003746043,0.00002773987,0.00001102784,0.963941,0.0005814505,0.03450537,0.0008074638,0.000006618857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004275695,0.0001919446,0.9940329,0.0002940103,0.00002677858,0.00001633156,0.000008502149,0.0000930961,0.001060787],"genre_scores_gemma":[0.3510819,0.001248168,0.6411119,0.0004323488,0.0001835306,0.0002837434,0.0001079459,0.0003009404,0.005249548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002137162,"threshold_uncertainty_score":0.01130259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00989813726740284,"score_gpt":0.2101253392150519,"score_spread":0.200227201947649,"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."}}