Full Text Available

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

Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights

Saved in:
Bibliographic Details
Published in:JMLR
Format: Online Article RSS Article
Published: 2026
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1864030190168965121
collection WordPress RSS
FRELIP Feed Integration
container_title JMLR
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:4331
institution FRELIP
journal_source_facet JMLR
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
subject_facet Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
title Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
title_auth Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
title_full Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
title_fullStr Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
title_full_unstemmed Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
title_short Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
title_sort posterior and variational inference for deep neural networks with heavy-tailed weights
topic Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v26/24-0894.html