{"id":"W4407900548","doi":"10.1109/tnnls.2025.3540014","title":"Conditional Mutual Information Constrained Deep Learning for Classification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mutual information; Artificial intelligence; Computer science; Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002854179,0.0001796389,0.0001961738,0.0001673974,0.001103956,0.0004290663,0.0002097054,0.0001284733,0.000004093005],"category_scores_gemma":[0.00001276217,0.000172609,0.00008955392,0.0004209262,0.00007298845,0.0005979321,0.00000344693,0.0005859946,0.000006368164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003661233,"about_ca_system_score_gemma":0.00002365586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001318974,"about_ca_topic_score_gemma":0.00000370996,"domain_scores_codex":[0.99871,0.0001640773,0.0003943378,0.0003058702,0.0001486155,0.0002771364],"domain_scores_gemma":[0.9988412,0.0005912182,0.0001728157,0.00017524,0.0001363043,0.00008328376],"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":[0.00001947384,0.00002006856,0.00004008381,0.00002589955,0.00002384507,2.902933e-7,0.00007157135,0.8930396,0.00007563461,0.01900213,0.0002113977,0.08746995],"study_design_scores_gemma":[0.0004888499,0.0001302218,0.000420523,0.00004299387,0.00001945321,0.00001584106,0.0001732142,0.9853182,0.00001792963,0.0001002931,0.01311165,0.0001608236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003347491,0.0001186718,0.9938269,0.0007362525,0.0007884194,0.0005673718,0.000004055307,0.0002888615,0.00032194],"genre_scores_gemma":[0.9978657,0.00005411225,0.0005728044,0.0002631182,0.0001033589,0.0003346176,0.00004044465,0.000008550091,0.0007573129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9945182,"threshold_uncertainty_score":0.8490848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270258337665602,"score_gpt":0.2413899696710828,"score_spread":0.2286873862944268,"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."}}