{"id":"W4383503423","doi":"10.1109/tnnls.2023.3289158","title":"Deep Multirepresentation Learning for Data Clustering","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Clustering high-dimensional data; Artificial intelligence; Embedding; AKA; Pattern recognition (psychology); Benchmark (surveying); Correlation clustering; Subspace topology; Cluster (spacecraft); Data mining; Data point; Geography","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":[0.0004409339,0.0001337476,0.0001545855,0.0001366127,0.0007753791,0.0003251975,0.0002941827,0.00008585145,0.000002849428],"category_scores_gemma":[0.00001924251,0.0001248822,0.00004600933,0.0003364211,0.00001805365,0.000558839,0.00001289414,0.000418255,0.00001667973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001113081,"about_ca_system_score_gemma":0.000005573808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008485533,"about_ca_topic_score_gemma":0.00001640147,"domain_scores_codex":[0.9986306,0.0002254919,0.0002264723,0.0004742964,0.0001589588,0.0002841408],"domain_scores_gemma":[0.9989867,0.0005037442,0.00009381698,0.0002912096,0.00004454136,0.00008001578],"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":[0.00001537696,0.000008099956,0.00004765054,0.00003043151,0.00001167138,0.000002772364,0.0002142779,0.861967,0.0001663865,0.000004507538,0.00015746,0.1373744],"study_design_scores_gemma":[0.0003682502,0.0001098078,0.0000961796,0.00007860903,0.00001069122,0.00001487286,0.000344894,0.9973778,0.00003186386,0.000003066046,0.001419687,0.0001443005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01244856,0.00009284094,0.98495,0.0001713997,0.001485071,0.0003075438,0.000001478264,0.0005175123,0.00002556692],"genre_scores_gemma":[0.9978369,0.0001266838,0.0007836626,0.00003791385,0.0001428547,0.00008698024,0.00004112686,0.00001949,0.000924412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9853883,"threshold_uncertainty_score":0.5963668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05200344600635538,"score_gpt":0.2925107670119717,"score_spread":0.2405073210056163,"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."}}