OptNet, end-to-end task-based learning, and control (ISMP)

2018 • Powerpoint • PDF

Abstract. Deep learning and end-to-end architectures provide a general and powerful way of implementing most modern machine learning tasks with a relatively small set of differentiable operations. These operations are usually simple affine operations composed with pointwise nonlinearities like the ReLU or sigmoid function. While general and successful, the drawbacks of these operations are plentiful, as the resulting learned modules can be uninterpretable and difficult to inject domainspecific knowledge into. This talk presents OptNet, a new paradigm for deep learning that integrates the solution of optimization problems "into the loop." OptNet allows domain knowledge in the form of learnable constrained optimization problems to be integrated into larger end-to-end architectures. We will first discuss the new OptNet primitive operations in the form of learning the parameters of a constrained convex quadratic program from data. Then we will show applications of applying these primitive operations in non-convex stochastic optimization and control.