{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004370518,0.001493408,0.002223691,0.003076406,0.001009523,0.00249955,0.002456217,0.002524575,0.003303198],"category_scores_gemma":[0.01805341,0.0007535107,0.001449312,0.002800884,0.003577206,0.003644633,0.004314528,0.002485112,0.00138226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002603799,"about_ca_system_score_gemma":0.001262183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002442789,"about_ca_topic_score_gemma":0.002696694,"domain_scores_codex":[0.9951175,0.002005617,0.0003163906,0.001124444,0.001181469,0.0002545157],"domain_scores_gemma":[0.993283,0.003719681,0.0007276073,0.001199647,0.0008111729,0.0002587608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002448613,0.00007797451,0.002813446,0.0006218716,0.0003464713,0.0001963591,0.000466879,0.4610727,0.003982395,0.36152,0.007903677,0.1607533],"study_design_scores_gemma":[0.00001115756,0.00005188595,0.0008670641,0.00006003002,0.00002548333,0.0001274345,0.00004169352,0.7141569,0.001502596,0.2790116,0.004088545,0.00005565907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01110876,0.001446365,0.9825068,0.0004508561,0.0000495021,0.00006027058,0.0003170258,0.0005348451,0.003525537],"genre_scores_gemma":[0.6078752,0.002185882,0.3770489,0.0008029923,0.0004511401,0.0005158667,0.002356274,0.0008609942,0.007902664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004370518,"threshold_uncertainty_score":0.02311385,"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."}}