|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'501'711
Articles rated: 2609
20 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Hyper-parameter optimization of Deep Convolutional Networks for object recognition | | Sachin S. Talathi
; | | Date: |
30 Jan 2015 | | Abstract: | Recently sequential model based optimization (SMBO) has emerged as a
promising hyper-parameter optimization strategy in machine learning. In this
work, we investigate SMBO to identify architecture hyper-parameters of deep
convolution networks (DCNs) object recognition. We propose a simple SMBO
strategy that starts from a set of random initial DCN architectures to generate
new architectures, which on training perform well on a given dataset. Using the
proposed SMBO strategy we are able to identify a number of DCN architectures
that produce results that are comparable to state-of-the-art results on object
recognition benchmarks. Specifically, we report three DCN networks generated by
our proposed algorithm that produce <9% test error rate, with the best network
exhibiting a test error rate of 7.81% on the CIFAR-10 benchmark. Our results
compare favorably to the current state-of-the-art of 7.97 % test error rate for
CIFAR-10 that are obtained by hand tuning. | | Source: | arXiv, 1501.7645 | | 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 Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER