Graph Convolutional Neural Networks for Molecule Generation and Travelling Salesman Problem


In this talk, I will discuss how to apply graph convolutional neural networks to quantum chemistry and operational research. The same high-level paradigm can be applied to generate new molecules with optimized chemical properties and to solve the Travelling Salesman Problem. The proposed approach consists of two steps. First, a graph ConvNet is used to auto-encode molecules and estimate TSP solutions in one-shot. Second, beam search is applied to the output of neural networks to produce a valid chemical or combinatorial solution. Numerical experiments demonstrate the performances of this learning system.

May 21, 2019 12:00 AM
Institute for Pure and Applied Mathematics, UCLA
Los Angeles, CA
Xavier Bresson
Assoc. Professor of Computer Science

Xavier Bresson is Associate Professor in Computer Science at NTU, Singapore. He is a leading researcher in the field of Graph Deep Learning, a new framework that combines graph theory and deep learning techniques.