{"id":"W2920348561","doi":"10.48550/arxiv.1903.02639","title":"IMEXnet: A Forward Stable Deep Neural Network","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Robustness (evolution); Residual; Convolutional neural network; Artificial intelligence; Convolution (computer science); Generalization; Key (lock); Segmentation; Artificial neural network; Deep learning; Sensitivity (control systems); Pixel; Limit (mathematics); Field (mathematics); Algorithm; Machine learning; Pattern recognition (psychology); 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":[0.0006871474,0.001549125,0.0007741163,0.0006971386,0.0005639921,0.001008095,0.003214319,0.001463671,0.004914347],"category_scores_gemma":[0.001799481,0.0006019985,0.0007458726,0.0008060034,0.0006591025,0.001843715,0.001815031,0.001900401,0.002813536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132766,"about_ca_system_score_gemma":0.001528638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009757478,"about_ca_topic_score_gemma":0.01856335,"domain_scores_codex":[0.9996133,0.00004857955,0.00001617297,0.0001464432,0.0001280567,0.00004740482],"domain_scores_gemma":[0.9996675,0.0000666804,0.00003574558,0.0001108565,0.00009586981,0.0000233345],"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":[0.0003698516,0.0002131887,0.001632137,0.0002864937,0.0001917054,0.0002139206,0.00006541939,0.5180691,0.01198895,0.02786415,0.07822894,0.3608761],"study_design_scores_gemma":[0.00002324292,0.00003491605,0.0001662968,0.00001037042,0.00001039369,0.00002963078,0.000006458014,0.9787989,0.00460881,0.009159159,0.007141586,0.00001027232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06038874,0.002448583,0.8795062,0.001323139,0.0006516668,0.0002667939,0.008025759,0.03249221,0.01489681],"genre_scores_gemma":[0.4053031,0.001162832,0.5269432,0.0009474097,0.0002262364,0.0007301641,0.02801983,0.002191874,0.03447537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009757478,"threshold_uncertainty_score":0.01940137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04425069276499816,"score_gpt":0.1820515040665877,"score_spread":0.1378008113015896,"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."}}