{"id":"W3192788685","doi":"","title":"A Variational Perspective on Diffusion-Based Generative Models and Score Matching","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Autoencoder; Generative model; Generative grammar; Matching (statistics); Diffusion; Computer science; Applied mathematics; Diffusion process; Bridging (networking); Function (biology); Score; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence; Statistics; Machine learning; Deep learning; Physics","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":[0.00009942889,0.0001506249,0.0001468366,0.00008653668,0.0003157572,0.0001200496,0.0002472635,0.00005464745,0.00003401956],"category_scores_gemma":[0.00003357457,0.0001556656,0.00007198715,0.000433669,0.00005632812,0.0005062566,0.0001774846,0.0001270176,0.000011131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001341047,"about_ca_system_score_gemma":0.0001652516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007510634,"about_ca_topic_score_gemma":0.00003738491,"domain_scores_codex":[0.9988227,0.0001752754,0.00008293201,0.0006439437,0.00008620814,0.0001888898],"domain_scores_gemma":[0.9990733,0.0002006829,0.00006142526,0.0003112849,0.0002489274,0.0001043553],"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":[0.00001196429,0.00005368639,0.0001178212,0.000001466544,0.00002184678,0.00009153754,0.0003181831,0.5480208,0.0004452617,0.4507131,0.00004112689,0.0001632197],"study_design_scores_gemma":[0.0004380874,0.00004088819,0.0009551478,0.00001779185,0.00001359887,0.000002683696,0.0002655071,0.8842969,0.0008551365,0.1129261,0.0000234604,0.0001646811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07531622,0.00004003429,0.9211249,0.0006349375,0.0001149931,0.00007467523,0.00000631011,0.00004817366,0.002639695],"genre_scores_gemma":[0.9831311,0.00002339683,0.0157116,0.0006473839,0.00007072763,5.542672e-7,0.000003885712,0.000007269849,0.0004041403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9078148,"threshold_uncertainty_score":0.6347858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05380895432927586,"score_gpt":0.1851875168588595,"score_spread":0.1313785625295837,"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."}}