Abstract

Deep learning techniques have achieved impressive performance in computer vision, natural language processing and speech analysis. These tasks focus on data that lie on Euclidean domains, and mathematical tools for these domains, such as convolution, downsampling, multi-scale, and locality, are well-defined and benefit from fast computational hardware like GPUs. However, many essential data and tasks deal with non-Euclidean domains for which deep learning methods were not originally designed, such as 3D point clouds and shapes, or functional MRI. The goals of this workshop are to: 1) bring together mathematicians, data scientists and domain experts to establish the current state of these emerging techniques, 2) discuss a framework for the analysis of these new deep learning techniques, 3) establish new research directions and applications of these techniques, and 4) discuss new computer processing architecture beyond GPU adapted to non-Euclidean domains