{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nearest-neighbour-score-estimators-for","title":"Nearest Neighbour Score Estimators for Diffusion Generative Models","arxiv_id":"2402.08018","date":"2024-02-12","proceeding":null,"authors":["Matthew Niedoba","Dylan Green","Saeid Naderiparizi","Vasileios Lioutas","Jonathan Wilder Lavington","Xiaoxuan Liang","Yunpeng Liu","Ke Zhang","Setareh Dabiri","Adam Ścibior","Berend Zwartsenberg","Frank Wood"],"abstract":"Score function estimation is the cornerstone of both training and sampling from diffusion generative models. 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