Speaker
Corey Adams
(Argonne National Laboratory)
Description
Machine learning in neutrino physics leverages many tools and techniques from the more mainstream areas of computer vision, but also brings new and interesting challenges. Notably, neutrino experiments have large images, typically with very high resolution, and often sparse or irregular data. In this talk I'll present several techniques that are successfully shown to accelerate machine learning for neutrino physics, including distributed learning, sparse and parallel IO, and tips for running on large scale systems.
