Introductory Overview Lecture: The Deep Learning Revolution — Invited Special Presentation
JSM Partner Societies
Organizer(s): Ryan Tibshirani, Carnegie Mellon University
Chair(s): Zaid Harchaoui, University of Washington
Deep Learning---broadly speaking, a class of methods based on many-layer neural networks---has witnessed an absolute explosion of interest in Machine Learning in recent years. It has proven to be an extremely useful tool in applications in computer vision, natural language processing, robotics and control, and many other areas. Even apart from these settings, many would argue that Deep Learning is the best "black-box, off-the-shelf" prediction method available.
Should Statisticians now be using Deep Learning for everything? Is this "black-box" really so easy to use, and moreover, can it be opened? Is there room for Statisticians to contribute to the understanding of and/or further development of Deep Learning "models"?
(Spoiler alert on this last question: YES! Come to the IOL to find out more!)
This Introductory Overview Lecture provides a comprehensive overview of some of the most popular/powerful Deep Learning methods, details their application in various data settings, and addresses the questions raised above. Talks will be given by Chris Manning and Ruslan Salakhutdinov, two of the foremost researchers today in Deep Learning. The session will be split into 4 talks of about 25 minutes each, giving by Chris and Ruslan in alternating in fashion.