How to design and train increasingly large neural network models is a topic that has been actively researched for several years. However, while there exists a large number of studies on training deeper and/or wider models, there is relatively little systematic research particularly on the effective usage of wide modular neural networks. Addressing this gap, and in an attempt to solve the problem of lengthy training times, we proposed Parallel Circuits (PCs), a biologically inspired architecture based on the design of the retina. In previous work we showed that this approach fails to maintain generalization performance in spite of achieving sharp speed gains. To address this issue, and motivated by the way dropout prevents node co-adaptation, in this paper, we suggest an improvement by extending dropout to the parallel-circuit architecture. The paper provides empirical proof and multiple insights into this combination. Experiments show promising results in which improved error rates are achieved in most cases, whilst maintaining the speed advantage of the PC approach.
Neural Processing Letters – Springer Journals
Published: Jul 22, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera