{"id":"W2970250826","doi":"","title":"Piecewise Strong Convexity of Neural Networks","year":2019,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Maxima and minima; Piecewise; Convexity; Artificial neural network; Mathematics; Differentiable function; Stochastic gradient descent; Convex function; Applied mathematics; Norm (philosophy); Open set; Regularization (linguistics); Mathematical optimization; Regular polygon; Algorithm; Computer science; Mathematical analysis; Discrete mathematics; Artificial intelligence; 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":[0.001372695,0.001103572,0.0008304802,0.0009671463,0.0004968184,0.001405856,0.001016653,0.001011045,0.001818851],"category_scores_gemma":[0.00762502,0.0005756735,0.0007854077,0.0005324216,0.001722872,0.002031112,0.001621687,0.001999219,0.0003529753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001339625,"about_ca_system_score_gemma":0.0005679157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002640161,"about_ca_topic_score_gemma":0.0009994664,"domain_scores_codex":[0.999397,0.0002157724,0.0000291118,0.0001003245,0.0001717718,0.00008596479],"domain_scores_gemma":[0.9975876,0.001430058,0.0002758928,0.0001723509,0.0003793435,0.0001547978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001169836,0.00002889368,0.00115158,0.0001496159,0.00005146579,0.000295414,0.0001431285,0.8183715,0.008623314,0.1557674,0.001465335,0.01383533],"study_design_scores_gemma":[0.000003505155,0.00003374086,0.000360548,0.00001020822,0.000004415012,0.00004310405,0.00001927414,0.9627016,0.001068594,0.03529018,0.0004580362,0.000006817775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1611451,0.0008442737,0.8264393,0.001455493,0.00004153145,0.00004344073,0.000303984,0.0003810313,0.009345694],"genre_scores_gemma":[0.945911,0.0007854903,0.04571058,0.0001670837,0.00006074486,0.0001038735,0.0003475014,0.0002355945,0.006678215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002640161,"threshold_uncertainty_score":0.009719729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416853856891275,"score_gpt":0.2348622963457124,"score_spread":0.2206937577767997,"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."}}