Top Deep Learning Papers

Top deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field.

By Matthew Mayo. 

Deep Learning

arXiv, maintained by Cornell University, is a popular open access academic paper preprint repository. It is an outlet for cutting edge research in numerous scientific fields, including machine learning. Mirroring the current general trend in academia, much of the recent posted machine learning research is deep learning related. 

Hugo Larochelle, PhD, is a Université de Sherbrooke machine learning professor (on leave), Twitter research scientist, noted neural network researcher, and deep learning aficiando. Since late summer 2015, he has been drafting and publicly sharing notes on arXiv machine learning papers that he has taken an interest in. At the time of this writing he has shared notes on 10 papers. 

A selection of 5 arXiv machine learning papers that Hugo has read and shared notes on follows. In an effort to help us better understand their content, for each paper an overview of its abstract along with an excerpt from Hugo’s notes are presented. It is hoped that having top deep learning papers explained by a noted expert in the field will make some of the more complex aspects of the science more approachable.