{"id":"W4391832947","doi":"10.48550/arxiv.2402.08018","title":"Nearest Neighbour Score Estimators for Diffusion Generative Models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Alliance de recherche numérique du Canada; Compute Canada; Lawrence Berkeley National Laboratory; Canadian Institute for Advanced Research; U.S. Department of Energy","keywords":"Estimator; Econometrics; Diffusion; Generative grammar; Generative model; Statistics; Mathematics; Computer science; Artificial intelligence; Statistical physics; Geography; Physics; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006755713,0.001059215,0.001574447,0.002302352,0.0008484268,0.002164122,0.003577135,0.002433192,0.003988171],"category_scores_gemma":[0.04262355,0.0009440649,0.00119292,0.001562775,0.002423829,0.003949596,0.003008453,0.00368708,0.001652559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001662416,"about_ca_system_score_gemma":0.001393229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004082341,"about_ca_topic_score_gemma":0.005412367,"domain_scores_codex":[0.9972028,0.001346443,0.000110642,0.0004477744,0.000765397,0.0001269609],"domain_scores_gemma":[0.9877506,0.008519636,0.0007552037,0.001375061,0.001258655,0.0003409258],"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":[0.00008854685,0.00008018391,0.002978982,0.0001556876,0.0001203467,0.00007338327,0.0001724258,0.4943061,0.002642866,0.385112,0.003880798,0.1103886],"study_design_scores_gemma":[0.000007747452,0.00001151473,0.000281546,0.00001990972,0.000007741558,0.00002806309,0.000009965189,0.8799607,0.0005658967,0.117972,0.001114239,0.00002083482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003099082,0.0001583108,0.9957165,0.0001171639,0.00001974052,0.00001941672,0.00004093917,0.0001720723,0.0006567029],"genre_scores_gemma":[0.3109266,0.0009875543,0.6769885,0.0003571247,0.0002529674,0.0003923764,0.0009466788,0.0007222708,0.008425863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006755713,"threshold_uncertainty_score":0.03572804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1122222746815957,"score_gpt":0.223446374483258,"score_spread":0.1112240998016623,"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."}}