Concur w/Brian. While the authors present genuine contributions,
meta-learning doesn't apply well to zero-sized architectures.
I didn't get a lot from the article, the arxiv link for the work done is
https://arxiv.org/abs/1812.00332
Best,
-Chaz
On Sun, Mar 24, 2019 at 4:17 PM Brian Lee
wrote:
Hi Simon,
Thanks for sharing. In my opinion, apart from discretizing the search
space, the N-Tuple system takes a very intuitive approach to
hyper-parameter optimization. The github repo readme notes you're working
on an extended version to handle continuous parameters, what's your general
Hi Brian,
Thanks for sharing your genuinely interesting result. One question though:
why would you train on a non-"zero" program? Do you think your program as a
result of your rules would perform better than zero, or is it imitating the
best known algorithm inconvenient for your purposes?
Best,
Rémi,
Nvidia launched the K20 GPU in late 2012. Since then, GPUs and their
convolution algorithms have improved considerably, while CPU performance
has been relatively stagnant. I would expect about a 10x improvement with
2016 hardware.
When it comes to training, it's the difference between
