Enough said. Courtesy by Edward Grefenstette.
This notes is for Stanford's Graphical Model class or cs228 2016-2017. As you know, the same course was taught by Daphne Koller back 5 years ago at Coursera, and it was known to be a very difficult class. So these notes are useful for learners who try to go through the class.
One thing new about the 2016 class is its stress on how graphical model can be used in topics of deep learning. For example, the part about how to train variational autoencoders would worth your time.
Image segmentation has always been a topic of computer vision. And recent advance of MaskRCNN (discussed in Issue 6) brought a lot of excitement to the community. This piece from Dhruv Parthasarathy, gives a fairly concise history of MaskRCNN from its origin of RCNN, and is widely circulated within Facebook and Twitterverse.
Btw, another good source of learning about image segmentation is Kapathy's cs231n Lecture 8 and 13. Johnson has done a very nice job to describe relationship between object segmentation and detection models such as the RCNN family.
ICLR 2017 was held at Toulon France from Apr 24 to 26. The link points to a list of Google's papers, which include two best papers. I (Arthur) found that openreview.net forum has more interesting discussion. But if you are want to see summaries, check out here [here] and some statistics here.
Here is list of GANs, collected by Avinash Hindupur, it seems to be one of most comprehensive lists we've seen so far. Another companion literature list could be really-awesome-ganhttps.
Deep Mind CEO, Demis Hassabis, review the book Deep Thinking written by Gary Kasparov, which would be released in May 2, 2017. Hassabis, himself a world-class chess player (with Elo Rating 2300 when he was 13), wrote about Kasparov's chess prowess, and his later embrace of the technology which defeat him.
In these days, any chess engine in your pocket can beat Magnus Carlsen. But it still left you in wonder extraordinary humans like Kasparov can intuit how super-engineered machine works and tell us more about its limitation. That's why we are looking forward for his book and you should definitely check out Hassabis' review.
This happened about 3 weeks ago, but we found it interesting enough to bring up - Ethics in NLP is a conference with focused on ethical challenges in NLP. So some sample paper includes "Gender and Dialect Bias in YouTube’s Automatic Captions" and it could be helpful for researchers who want to understand intrinsic bias of machine learning algorithms.