Despite neural networks' impressive performance on many tasks, designing efficient and deploying neural networks stands an arduous challenge. It involves making a lot of discrete decisions, such as which models to use and how to deploy such models. Automatizing these designs thus comes at a great benefit. In this talk, I will show how one can view the task of designing and deploying neural networks as a combinatorial optimization problem. Then, I will discuss an application of deep reinforcement learning (DRL) on a canonical combinatorial optimization task: the Traveling Salesman Problem (TSP), which outperforms many existing heuristics. Finally, I will connect the dots, showing that the same DRL approach on TSP can be applied to automatize the process of designing and deploying neural networks. Time permits, I will discuss the existing challenges and some future directions in this line of work.
AI Seminar is generously sponsored by Apple.