{"id":"W3172084791","doi":"10.48550/arxiv.2002.03629","title":"Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Speedup; Computer science; Computation; Feed forward; Parallelizable manifold; Parallel computing; Feedforward neural network; Nonlinear system; Artificial neural network; Algorithm; Artificial intelligence; Control engineering","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":[0.0005863575,0.000958511,0.0007238942,0.0005263158,0.0004449431,0.0006847787,0.00109372,0.0007197758,0.005239312],"category_scores_gemma":[0.002468099,0.0003921191,0.0006181944,0.0005630133,0.0005546047,0.0009852283,0.001117493,0.001148675,0.001233951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008239367,"about_ca_system_score_gemma":0.001274867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006928913,"about_ca_topic_score_gemma":0.01029762,"domain_scores_codex":[0.9996029,0.00007852236,0.00002390295,0.00007882059,0.0001673959,0.00004848578],"domain_scores_gemma":[0.9991347,0.000401688,0.0000477756,0.0001682447,0.0002176115,0.00002994841],"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":[0.0001398027,0.00009166335,0.0009050684,0.0001992394,0.00007003824,0.0001463475,0.0001539796,0.7517421,0.0163709,0.03361705,0.005799858,0.190764],"study_design_scores_gemma":[0.00001251656,0.000007517032,0.000045616,0.000003207047,0.000003889029,0.00001259476,0.000006174061,0.9902741,0.002541121,0.006175424,0.0009147804,0.000003035916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02287228,0.000161312,0.9672542,0.0002199765,0.00008155029,0.00005146816,0.00009008562,0.003752947,0.005516156],"genre_scores_gemma":[0.295574,0.000157776,0.6982151,0.0001399187,0.00005506336,0.0001855758,0.000286012,0.0004960016,0.004890553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006928913,"threshold_uncertainty_score":0.01752722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1431964043180718,"score_gpt":0.2200145404521108,"score_spread":0.07681813613403904,"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."}}