| | |
| | |
Stat |
Members: 3643 Articles: 2'487'895 Articles rated: 2609
29 March 2024 |
|
| | | |
|
Article overview
| |
|
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference | Yarin Gal
; Zoubin Ghahramani
; | Date: |
6 Jun 2015 | Abstract: | We 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 |
|
|
No review found.
Did you like this article?
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
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |