{"id":"W4403965030","doi":"10.48550/arxiv.2408.03407","title":"Deep Clustering via Distribution Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Cluster analysis; Artificial intelligence; Distribution (mathematics); Computer science; Deep learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001727522,0.001118723,0.001402074,0.002045473,0.0009501935,0.001755125,0.002696106,0.00177561,0.00328643],"category_scores_gemma":[0.0049187,0.000710209,0.001407125,0.001939964,0.001722587,0.003388094,0.002694402,0.002385783,0.001504878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002948294,"about_ca_system_score_gemma":0.002035441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006810057,"about_ca_topic_score_gemma":0.00858202,"domain_scores_codex":[0.9986613,0.0003306167,0.00005793791,0.0004543567,0.0003365878,0.0001592031],"domain_scores_gemma":[0.9982184,0.000568773,0.0001847928,0.0004718404,0.0004457057,0.0001104807],"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":[0.0001581254,0.0001165912,0.002323766,0.0002009845,0.0001366486,0.00007618145,0.0001872196,0.6113874,0.00584281,0.09190431,0.008700585,0.2789654],"study_design_scores_gemma":[0.000007198513,0.00001264628,0.0001976548,0.000009120719,0.000007423512,0.00002823365,0.00001861312,0.9594514,0.001712456,0.03724906,0.001295322,0.00001089562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007819924,0.0002501523,0.9890309,0.0002603476,0.00002515798,0.00003044127,0.0001374951,0.001166513,0.001279042],"genre_scores_gemma":[0.4738492,0.0008410604,0.5127702,0.0006505377,0.000132863,0.0002940523,0.001939498,0.0007132791,0.008809376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006810057,"threshold_uncertainty_score":0.02139151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261388414991503,"score_gpt":0.1804149686870661,"score_spread":0.1378010845371511,"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."}}