Dec 22, 2016
Improving the Scaling of Deep Learning Networks by Characterizing and Exploiting Soft Convexity
ABOUT THE PROJECT
At a glance
The main goal of this project is to improve the scaling properties of DNNs through a non-traditional approach. In particular, the team will use ideas from statistical mechanics to define a notion of soft convexity, the soft curvature of which can be controlled by adjusting various knobs that define the DNN. The team intends to make this precise and to develop proof-of-principle implementations on image, video, and other related data to illustrate the benefits of this approach.
|Michael Mahoney||Deep Neural Networks|
BAIR/CPAR/BDD Internal Weekly Seminar
The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and the Berkeley Deep Drive Consortium. The seminars are every Friday afternoon in room 250 Sutardja Dai Hall from 3:10-4:10 PM, and are open to BAIR/BDD faculty, students, and sponsors. Seminars will be webcast live and recorded talks will be available online following the seminar.