{"id":"W2891477799","doi":"","title":"Deep Homogeneous Mixture Models: Representation, Separation, and Approximation","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Representation (politics); Connection (principal bundle); Constant (computer programming); Computer science; Mixture model; Homogeneous; Exponential function; Tree (set theory); Graphical model; Algorithm; Mathematics; Exponential growth; Latent variable; Artificial intelligence; Pattern recognition (psychology); Combinatorics; Mathematical analysis","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":[0.002308979,0.00128546,0.001097825,0.001226629,0.0005040711,0.001821099,0.002304329,0.001674293,0.002353802],"category_scores_gemma":[0.009725031,0.00102701,0.001208895,0.001395469,0.001483119,0.00526575,0.002916523,0.003896965,0.001031825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001864692,"about_ca_system_score_gemma":0.001344858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006072852,"about_ca_topic_score_gemma":0.007444084,"domain_scores_codex":[0.9991374,0.0003517122,0.00004317404,0.0001743214,0.0002053775,0.00008808613],"domain_scores_gemma":[0.9975023,0.001398002,0.0002889502,0.0004453318,0.0002534195,0.0001120171],"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":[0.0001108404,0.00006229214,0.001178252,0.0001341083,0.0000627031,0.00007012362,0.0002566025,0.5153992,0.004023932,0.3524601,0.002984287,0.1232576],"study_design_scores_gemma":[0.000002568219,0.000006380222,0.00005989632,0.000008783044,0.000005629785,0.00001699557,0.000006395281,0.9361571,0.0005641707,0.06259683,0.0005689348,0.000006266874],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002507366,0.0001341336,0.9966922,0.0000992526,0.000006798693,0.000009351333,0.00003540108,0.0001793919,0.0003360395],"genre_scores_gemma":[0.2643964,0.001028125,0.7284112,0.000249931,0.0001115597,0.0002198497,0.0005848029,0.0003712936,0.004626756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006072852,"threshold_uncertainty_score":0.0135293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601520224338223,"score_gpt":0.2934103046597554,"score_spread":0.2673951024163732,"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."}}