{"id":"W4391386721","doi":"10.3390/e26020129","title":"Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks","year":2024,"lang":"en","type":"article","venue":"Entropy","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"MNIST database; Computer science; Leverage (statistics); Regret; Machine learning; Artificial intelligence; Artificial neural network; Key (lock); Test set; Set (abstract data type); Training set; Active learning (machine learning); Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003553855,0.0001304149,0.0001672978,0.0001024624,0.00006927425,0.0001178941,0.0003563141,0.00007024313,0.000007326971],"category_scores_gemma":[0.00008125038,0.0001205959,0.00007612431,0.0002802154,0.00002806472,0.0002551846,0.00017768,0.0006064642,0.00001492044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005425184,"about_ca_system_score_gemma":0.00002174335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008593628,"about_ca_topic_score_gemma":0.000004413333,"domain_scores_codex":[0.9987369,0.000197881,0.0001921263,0.0003469895,0.0002309578,0.0002951092],"domain_scores_gemma":[0.9995288,0.0001650164,0.0000620174,0.0001669337,0.00002307008,0.00005417181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007194187,0.00003202982,0.0006832367,0.0000244476,0.0000323495,0.0000641354,0.003507584,0.1220112,0.00004890196,0.02226123,0.00006586295,0.8512619],"study_design_scores_gemma":[0.0001796654,0.00008381392,0.004451382,0.00005716497,0.000005237026,0.000004011482,0.0001798182,0.9910418,0.00007549335,0.0002531308,0.00354423,0.0001242935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06087477,0.007823025,0.9243204,0.002417797,0.002572017,0.0001655185,0.000004599265,0.0004967761,0.001325109],"genre_scores_gemma":[0.9982067,0.0002637204,0.001139974,0.00002046684,0.0002792079,0.000006673782,0.00003299425,0.000009818513,0.00004043746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9373319,"threshold_uncertainty_score":0.4917759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02338040948801737,"score_gpt":0.2668676243540459,"score_spread":0.2434872148660286,"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."}}