{"id":"W4287728238","doi":"","title":"Neumann networks: differential programming for supervised learning with missing values","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Differential (mechanical device); Computer science; Artificial intelligence; Missing data; Supervised learning; Machine learning; Semi-supervised learning; Artificial neural network; 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":[0.006152738,0.001420292,0.002191043,0.0008925914,0.0006918405,0.001991613,0.0033654,0.002958843,0.003401256],"category_scores_gemma":[0.01893478,0.00165337,0.001199324,0.001291521,0.002571587,0.003412417,0.005245152,0.005668384,0.0008865601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408289,"about_ca_system_score_gemma":0.002235032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002112393,"about_ca_topic_score_gemma":0.002555519,"domain_scores_codex":[0.9970674,0.001727787,0.0001212966,0.0004603918,0.0004881014,0.0001349562],"domain_scores_gemma":[0.9913245,0.006955748,0.0002919333,0.0004040646,0.0007047582,0.0003189109],"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":[0.0002347052,0.0001778158,0.0006126225,0.0004125626,0.0001662761,0.0001412621,0.0002371917,0.5660186,0.001507643,0.3446942,0.005707095,0.08009007],"study_design_scores_gemma":[0.00001091712,0.00001490972,0.00002366879,0.00001502265,0.000006152249,0.00001097672,0.000007432857,0.8881934,0.0002377815,0.1109153,0.0005570658,0.000007375787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002365313,0.0002286248,0.9961825,0.0003078215,0.00005736109,0.000034063,0.00006329512,0.00008024828,0.0006807706],"genre_scores_gemma":[0.2644051,0.001330714,0.7161326,0.0007372064,0.0004932759,0.001179305,0.000827956,0.0004290702,0.01446483],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006152738,"threshold_uncertainty_score":0.03253913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040722626228698,"score_gpt":0.2358127269990686,"score_spread":0.2154055007367816,"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."}}