Science-advisor
REGISTER info/FAQ
Login
username
password
     
forgot password?
register here
 
Research articles
  search articles
  reviews guidelines
  reviews
  articles index
My Pages
my alerts
  my messages
  my reviews
  my favorites
 
 
Stat
Members: 3643
Articles: 2'487'895
Articles rated: 2609

29 March 2024
 
  » arxiv » 1506.2158

 Article overview


Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Yarin Gal ; Zoubin Ghahramani ;
Date 6 Jun 2015
AbstractWe present an efficient Bayesian convolutional neural network (convnet). The model offers better robustness to over-fitting on small data than traditional approaches. This is by placing a probability distribution over the convnet’s kernels (also known as filters). We approximate the model’s intractable posterior with Bernoulli variational distributions. This requires no additional model parameters. Our model can be implemented using existing tools in the field. This is by extending the recent interpretation of dropout as approximate inference in the Gaussian process to the case of Bayesian neural networks. The model achieves a considerable improvement in classification accuracy compared to previous approaches. We finish with state-of-the-art results on CIFAR-10 following our new interpretation.
Source arXiv, 1506.2158
Services Forum | Review | PDF | Favorites   
 
Visitor rating: did you like this article? no 1   2   3   4   5   yes

No review found.
 Did you like this article?

This article or document is ...
important:
of broad interest:
readable:
new:
correct:
Global appreciation:

  Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.

browser claudebot






ScienXe.org
» my Online CV
» Free


News, job offers and information for researchers and scientists:
home  |  contact  |  terms of use  |  sitemap
Copyright © 2005-2024 - Scimetrica