AI News, MachineLearning
- On Thursday, October 4, 2018
- By Read More
Hyper-parameter optimization has already been found to be a useful way to (partially) automate the search for good configurations in deep learning.
One of the main early contributors to this line of work (before it was applied to machine learning hyper-parameter optimization) is Frank Hutter (along with collaborators), who devoted his PhD thesis (2009) to algorithms for optimizing knobs that are typically set by hand in general in software systems.
http://jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf We then proposed using for deep learning the kinds of algorithms Hutter had developed for other contexts, called sequential optimization and this was published at NIPS'2011, in collaboration with another PhD student who devoted his thesis to this work, Remi Bardenet, and his supervisor Balazs Kegl (previously a prof in my lab, now in France).
http://www.dmi.usherb.ca/~larocheh/publications/gpopt_nips.pdf Snoek et al put out a software that has since been used by many researchers, called 'spearmint', and I found out recently that Netflix has been using it in their new work aiming to take advantage of deep learning for movie recommendations: http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html
- On Friday, February 21, 2020
The Future of Deep Learning Research
Back-propagation is fundamental to deep learning. Hinton (the inventor) recently said we should "throw it all away and start over". What should we do?
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