NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control

Understand

In this work we propose the use of adaptive stochastic search as a building block for general, non-convex optimization operations within deep neural network architectures.

• Specifically, for an objective function located at some layer in the network and parameterized by some network parameters, we employ adaptive stochastic search to perform optimization over its output.
• This operation is differentiable and does not obstruct the passing of gradients during backpropagation, thus enabling us to incorporate it as a component in end-to-end learning.
• We study the proposed optimization module's properties and benchmark it against two existing alternatives on a synthetic energy-based structured prediction task, and further showcase its use in stochastic optimal control applications.