{"id":"W1408639475","doi":"","title":"Learning Recurrent Neural Networks with Hessian-Free Optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":534,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Hessian matrix; Sequence (biology); Computer science; Artificial intelligence; Artificial neural network; Term (time); Scheme (mathematics); Optimization problem; Interpretation (philosophy); Algorithm; Mathematics","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":[0.001514278,0.001060219,0.001021267,0.0004132419,0.000312735,0.0006274505,0.001299544,0.00134231,0.001403068],"category_scores_gemma":[0.00480813,0.0006698712,0.000525245,0.0004766549,0.0007487337,0.001417595,0.001103012,0.001341167,0.000726054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000559283,"about_ca_system_score_gemma":0.0008247065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003412586,"about_ca_topic_score_gemma":0.005583178,"domain_scores_codex":[0.9995554,0.0001979608,0.0000264779,0.00008775901,0.00009682867,0.0000354821],"domain_scores_gemma":[0.9988365,0.0007306528,0.0001222039,0.0001106835,0.0001605685,0.00003947685],"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":[0.00003565283,0.00002533566,0.000302051,0.00005543879,0.0000395213,0.00005312089,0.0000451604,0.9413835,0.001623497,0.01221866,0.001104219,0.04311385],"study_design_scores_gemma":[0.000002098622,0.000006428596,0.00001342165,0.000001356398,0.000001262798,0.00000300681,8.448557e-7,0.9980274,0.0001343395,0.001730794,0.00007736332,0.000001650701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01563589,0.000260803,0.9821745,0.0001572375,0.00003140769,0.0000220797,0.00003439133,0.0006124486,0.001071239],"genre_scores_gemma":[0.5103866,0.0003285779,0.4824223,0.0002981048,0.0001162404,0.0001934634,0.0004117901,0.0003686711,0.005474244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003412586,"threshold_uncertainty_score":0.008008301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02062545344930514,"score_gpt":0.2115079594753474,"score_spread":0.1908825060260423,"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."}}