Artificial Intelligence Seminar


From Neural Combinatorial Optimization to Automatic Machine Learning

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.