{"id":"W2963117168","doi":"","title":"Negative eigenvalues of the Hessian in deep neural networks","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hessian matrix; Eigenvalues and eigenvectors; Deep learning; Artificial neural network; Deep neural networks; Function (biology); Computer science; Hessian equation; Artificial intelligence; Matrix (chemical analysis); Work (physics); Applied mathematics; Mathematical optimization; Mathematics; Engineering; Mathematical analysis; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.000053942,0.00007837459,0.00008035886,0.00009623558,0.00006133912,0.00003410639,0.0002449695,0.00003798195,0.0001786196],"category_scores_gemma":[0.0001175705,0.00006567324,0.00004108769,0.0001621212,0.0001256919,0.00008337948,0.00004535582,0.0002414705,0.000008036674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003038504,"about_ca_system_score_gemma":0.000009142414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060357,"about_ca_topic_score_gemma":0.0001202821,"domain_scores_codex":[0.9993836,0.00006588502,0.0001698889,0.0001191836,0.0001602532,0.0001011531],"domain_scores_gemma":[0.9995311,0.00009304995,0.00005913432,0.0001506977,0.0001483278,0.00001772229],"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":[0.00004113822,0.00005657436,0.0628048,0.000003920247,0.00009133478,0.000006579926,0.004374993,0.8729175,0.007699789,0.03206656,0.0005565141,0.01938028],"study_design_scores_gemma":[0.0001023052,0.0000270484,0.05571064,0.00005937039,0.000003407657,0.000002236458,0.0003308312,0.9335391,0.007282823,0.00277537,0.00009930532,0.00006757713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.85795,0.00002633103,0.02411321,0.0007924046,0.001006561,0.0002124036,0.000003475511,0.0002656021,0.1156299],"genre_scores_gemma":[0.9992937,0.00001455715,0.0003450987,0.00003793265,0.0001052047,0.00001199987,0.000004510494,0.00001077018,0.0001762665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1413436,"threshold_uncertainty_score":0.2678077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03369257548278821,"score_gpt":0.3082430648912333,"score_spread":0.2745504894084451,"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."}}