Tuesday, November 28, 2017 - 12:00pm to 1:00pm
Location:3305 Newell-Simon Hall
Speaker:BRANDON AMOS, Ph.D. Student https://bamos.github.io/
Convex Optimization within Deep Learning
In this talk, we highlight two challenges present in today's deep learning landscape that involve adding structure to the input or latent space of a model. We will discuss how to overcome some of these challenges with the use of learnable optimization sub-problems that subsume standard architectures and layers. These architectures obtain state-of-the-art empirical results in many domains such as continuous action reinforcement learning and tasks that involve learning hard constraints like the game Sudoku.
We will cover topics from these two papers:
Input Convex Neural Networks, Brandon Amos, Lei Xu, J. Zico Kolter, ICML 2017
OptNet: Differentiable Optimization as a Layer in Neural Networks, Brandon Amos, J. Zico Kolter, ICML 2017
Joint work with J. Zico Kolter
Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.