Oleg Zabluda's blog
Sunday, December 18, 2016
 
CS 294: Deep Reinforcement Learning, Spring 2017
CS 294: Deep Reinforcement Learning, Spring 2017
Instructors: Sergey Levine, John Schulman, Chelsea Finn
http://rll.berkeley.edu/deeprlcourse/

The Nuts and Bolts of Deep RL Research, John Schulman, OpenAI, December 9th, 2016
http://rll.berkeley.edu/deeprlcourse/docs/nuts-and-bolts.pdf
http://rll.berkeley.edu/deeprlcourse

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- posted by Oleg Zabluda @ 4:06 PM | permalink | | | technorati | bloglines | feedster
 
50 things I learned at NIPS 2016
50 things I learned at NIPS 2016
https://blog.ought.com/nips-2016-875bb8fadb8c
https://blog.ought.com/nips-2016-875bb8fadb8c

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- posted by Oleg Zabluda @ 4:05 PM | permalink | | | technorati | bloglines | feedster
 
Image-to-Image Translation with Conditional Adversarial Networks (2016) Phillip Isola, Jun-Yan Zhu, Tinghui Zhou,...
Image-to-Image Translation with Conditional Adversarial Networks (2016) Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
"""
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
"""
https://arxiv.org/abs/1611.07004

https://phillipi.github.io/pix2pix/
https://phillipi.github.io/pix2pix

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- posted by Oleg Zabluda @ 3:15 PM | permalink | | | technorati | bloglines | feedster

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