Full Text Available

Note: Clicking the button above will open the full text document at the original institutional repository in a new window.

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

Saved in:
Bibliographic Details
Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1864030190246559751
collection WordPress RSS
FRELIP Feed Integration
container_title Journal of Machine Learning Research
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:5003
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
title_auth Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
title_full Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
title_fullStr Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
title_full_unstemmed Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
title_short Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
title_sort wasserstein convergence guarantees for a general class of score-based generative models
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v26/24-0902.html