{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001515688,0.0002160432,0.0001807023,0.0001354771,0.0001671674,0.0002113639,0.0007407155,0.0002478773,0.00004292874],"category_scores_gemma":[0.00001906113,0.0002443567,0.0001646711,0.0003788459,0.00003510038,0.000248502,0.003182524,0.0008634847,0.000660329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001740546,"about_ca_system_score_gemma":0.00005587087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000649636,"about_ca_topic_score_gemma":0.00002447342,"domain_scores_codex":[0.9985843,0.00009671497,0.0001382464,0.0008359966,0.00007821363,0.000266527],"domain_scores_gemma":[0.9991745,0.00004396471,0.0001103787,0.0004768161,0.00008223288,0.0001120829],"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":[0.00001961169,0.00004244177,0.0003137391,0.0002721712,0.00007682756,0.0005107793,0.0003194763,0.9613113,0.0002436163,0.01683736,0.0008285072,0.01922417],"study_design_scores_gemma":[0.0001169502,0.00002271622,0.0001011162,0.0002031171,0.00003408868,0.000006384754,0.00003581533,0.957116,0.0001884659,0.0405328,0.001367336,0.000275171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04758572,0.00008927962,0.9489031,0.0001067952,0.0009631581,0.000124619,0.000006162102,0.00051529,0.001705814],"genre_scores_gemma":[0.9972958,0.000112375,0.0009359253,0.00003448563,0.00008175719,0.000001092979,0.00009605918,0.00001319258,0.001429317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9497101,"threshold_uncertainty_score":0.9964577,"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."}}