{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002935097,0.001349717,0.001238867,0.001339318,0.000667284,0.002367056,0.002309022,0.002972966,0.004258512],"category_scores_gemma":[0.008981875,0.001038725,0.001518334,0.001000748,0.004672326,0.00402964,0.003043897,0.004402121,0.0005932367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002473917,"about_ca_system_score_gemma":0.001377787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004124011,"about_ca_topic_score_gemma":0.003044804,"domain_scores_codex":[0.9989078,0.0004333714,0.00004463644,0.0002747503,0.0002355727,0.0001038419],"domain_scores_gemma":[0.9967862,0.002140321,0.0002989051,0.0003352489,0.0002367887,0.0002025917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001256623,0.00001207741,0.0002699616,0.00004085173,0.00002870276,0.00004327413,0.00005683082,0.1183145,0.0006751991,0.8740299,0.0006899186,0.005826262],"study_design_scores_gemma":[0.000008720093,0.00001796798,0.0001313519,0.0000223785,0.000009095282,0.00005114879,0.0000121033,0.5395938,0.0003029106,0.4578388,0.001993565,0.00001820456],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004357897,0.0006711512,0.9894135,0.001280367,0.00006342846,0.00001574673,0.00007821815,0.00006628381,0.004053402],"genre_scores_gemma":[0.6395351,0.003623491,0.3306926,0.001171411,0.0007944949,0.0002295601,0.000407462,0.0003977185,0.0231481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004258512,"threshold_uncertainty_score":0.01794964,"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."}}