{"id":"W4382318270","doi":"10.1609/aaai.v37i8.26145","title":"Diffusing Gaussian Mixtures for Generating Categorical Data","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Categorical variable; Computer science; Generative grammar; Generative model; Probabilistic logic; Domain (mathematical analysis); Artificial intelligence; Gaussian; Mixture model; Machine learning; Focus (optics); Data mining; Mathematics","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":[0.006140451,0.0008172382,0.0008747977,0.001427578,0.0005859603,0.001617984,0.00184092,0.001617091,0.002609515],"category_scores_gemma":[0.0211356,0.0005274601,0.001098234,0.001145835,0.001607651,0.002558404,0.002173233,0.002817953,0.0008030609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001945742,"about_ca_system_score_gemma":0.0008491029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003283158,"about_ca_topic_score_gemma":0.003157754,"domain_scores_codex":[0.9980362,0.0009781616,0.00007158551,0.000346457,0.0004722769,0.00009519222],"domain_scores_gemma":[0.9907619,0.006821935,0.0004546664,0.001081471,0.0006387174,0.0002411619],"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":[0.0002425699,0.00008620817,0.002713623,0.0001547652,0.000071169,0.0000986532,0.0002078645,0.8177719,0.004880931,0.1074784,0.002354253,0.06393967],"study_design_scores_gemma":[0.00001227304,0.00002237368,0.000158977,0.0000105295,0.000005099957,0.00003298499,0.0000116565,0.9734528,0.001664194,0.02400187,0.0006141269,0.0000131339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01584558,0.0002150574,0.9817991,0.0003311902,0.0000372229,0.00009092676,0.0001885993,0.0006417087,0.0008506986],"genre_scores_gemma":[0.5387798,0.0004418539,0.4561494,0.0003899873,0.0000735038,0.0003718622,0.001298493,0.0002943828,0.002200703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006140451,"threshold_uncertainty_score":0.03247422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1648906144388331,"score_gpt":0.3306756981985165,"score_spread":0.1657850837596834,"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."}}