Trainees will learn the architecture behind modern classification networks, expanding the power of the problems that can be solved, while increasing the computational complexity required for training.
About the Speaker
Corey Adams is an assistant computer scientist at the Argonne Leadership Computing Facility. Originally a high-energy physicist working on neutrino physics problems, he now works on applying deep learning and machine learning techniques to science problems – and still neutrino physics – on high-performance computers. He has experience in classification, segmentation, and sparse convolutional neural networks as well as running machine learning training at scale.