{"id":"W4306246352","doi":"10.48550/arxiv.2210.06300","title":"Generalised Mutual Information for Discriminative Clustering","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier de l'Université Laval","funders":"","keywords":"Mutual information; Cluster analysis; Computer science; Divergence (linguistics); Context (archaeology); Artificial intelligence; Discriminative model; A priori and a posteriori; Artificial neural network; Set (abstract data type); Information theory; Relevance (law); Kullback–Leibler divergence; Property (philosophy); Machine learning; Data mining; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001621179,0.0001817332,0.000172095,0.0002417567,0.0002598141,0.0001441142,0.0009524492,0.0001223335,0.0000741472],"category_scores_gemma":[0.00002870522,0.000209031,0.0001583014,0.0002242803,0.00002893258,0.001184941,0.002009558,0.0002778267,0.00003736676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001839556,"about_ca_system_score_gemma":0.0000964392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005957194,"about_ca_topic_score_gemma":0.00001335933,"domain_scores_codex":[0.9990059,0.00007901932,0.0001649115,0.0004470001,0.00008463345,0.0002185588],"domain_scores_gemma":[0.9990197,0.00007006573,0.0002193391,0.0004855902,0.0001265941,0.00007865045],"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":[0.000187596,0.000133745,0.0001847999,0.0004060942,0.000128573,0.00005130764,0.005904033,0.8575687,0.0001845041,0.1115175,0.009970825,0.0137623],"study_design_scores_gemma":[0.000588373,0.00006637401,0.0001329298,0.00004274502,0.00003028295,0.000001246552,0.0005299907,0.9751503,0.0002952909,0.01616865,0.006666878,0.0003268933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06813884,0.000007350189,0.9278226,0.0001444818,0.0008692638,0.0004469076,0.00009036194,0.0001685908,0.002311596],"genre_scores_gemma":[0.9904381,0.00004400762,0.007687241,0.0002843753,0.00005642371,0.0000160865,0.0003250674,0.000009375244,0.001139302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9222993,"threshold_uncertainty_score":0.8524038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09679593961579883,"score_gpt":0.2024392771331614,"score_spread":0.1056433375173626,"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."}}