{"id":"W2101110010","doi":"10.1016/j.cogsys.2006.02.001","title":"Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning","year":2006,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut Philippe Pinel de Montréal; Université du Québec en Outaouais; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Connectionism; Unsupervised learning; Competitive learning; Artificial intelligence; Computer science; Odds; Machine learning; Associative property; Associative learning; Cognition; Artificial neural network; Benchmark (surveying); Psychology; Cognitive 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":[0.002144438,0.0002140009,0.0003064349,0.0001472738,0.0008114674,0.0002939117,0.000771513,0.0001156405,0.000009728015],"category_scores_gemma":[0.0003031119,0.0001484686,0.000117343,0.0006658051,0.0003394201,0.0001846594,0.0002820644,0.0009632046,0.00004911391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009246745,"about_ca_system_score_gemma":0.00002913322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002380191,"about_ca_topic_score_gemma":0.00000721065,"domain_scores_codex":[0.995034,0.002593492,0.0003505371,0.0005082801,0.0008651848,0.0006484786],"domain_scores_gemma":[0.996722,0.002449169,0.0001365809,0.0003944895,0.0001927132,0.0001050063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001373247,0.001514741,0.349666,0.001218557,0.0006233239,0.0007592055,0.002658123,0.3154352,0.03167839,0.03542769,0.002493238,0.2571523],"study_design_scores_gemma":[0.0008866032,0.0007648816,0.03194083,0.0004674367,0.00001497916,0.00001642695,0.0006724467,0.9624668,0.002384948,0.00009842245,0.00005779782,0.0002284537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7060626,0.001824913,0.2898246,0.0001668258,0.0001979195,0.0008347363,0.00005196558,0.00009767369,0.0009388112],"genre_scores_gemma":[0.9994563,0.00001572379,0.00001316664,0.00001287435,0.0001804548,0.0001275621,0.00003541622,0.00001752715,0.0001409322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6470315,"threshold_uncertainty_score":0.6241233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04337926785097943,"score_gpt":0.3089418547753327,"score_spread":0.2655625869243532,"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."}}