{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002106058,0.001218662,0.001667186,0.002167471,0.0008022235,0.001635447,0.002250217,0.001649034,0.001716808],"category_scores_gemma":[0.004324093,0.0006161195,0.001602952,0.00300917,0.001301911,0.003277143,0.002933834,0.002732832,0.001058431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002332103,"about_ca_system_score_gemma":0.001890067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004457008,"about_ca_topic_score_gemma":0.006145357,"domain_scores_codex":[0.9983131,0.0005476037,0.0001156603,0.0004695511,0.0003956463,0.0001584353],"domain_scores_gemma":[0.9985947,0.0003985733,0.0001873186,0.0004482464,0.0002808504,0.00009024871],"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":[0.0001532649,0.0001943993,0.001839922,0.0002652679,0.0002407828,0.00009221349,0.0002453612,0.48788,0.006032498,0.04175933,0.007649304,0.4536477],"study_design_scores_gemma":[0.000004975487,0.00002279614,0.0001791789,0.00001254356,0.00001073111,0.00002179083,0.00002387794,0.9691665,0.001533633,0.02787703,0.001135184,0.00001182466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005242698,0.0004984873,0.9925547,0.0001902087,0.00002567621,0.00002564418,0.0001047201,0.0008423994,0.0005154461],"genre_scores_gemma":[0.3668118,0.001044217,0.6254064,0.0004617774,0.0001127541,0.0002498185,0.00177465,0.0002551206,0.003883388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004457008,"threshold_uncertainty_score":0.01692069,"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."}}