Title:  Democratizing Deep Learning Development with DeepHyper

Abstract:  In recent years, deep neural networks (DNNs) have achieved considerable success in learning complex nonlinear relationships between features and targets from large datasets. Nevertheless, designing high-performing DNN architecture for a given data set is an expert-driven, time-consuming, trial-and-error manual task. A major bottleneck in the construction of DNNs is the vast search space of architectures that need to be explored in the face of new data sets. Moreover, DNNs typically require user-specified values for hyperparameters, which strongly influence performance factors such as training time and prediction accuracy. In this talk, we will introduce participants to DeepHyper, a scalable automated machine learning package for developing deep neural networks. DeepHyper provides an infrastructure that targets experimental research in neural architecture search (NAS) and hyperparameter search (HPS) methods, scalability, and portability across diverse supercomputers through the use of the different workflow manager.

Speaker: Prasanna Balaprakash, Computer Scientist, Argonne National Laboratory

Bio: Prasanna Balaprakash is a computer scientist with a joint appointment in the Mathematics and Computer Science Division and the Leadership Computing Facility at Argonne National Laboratory. His research work spans the areas of artificial intelligence, machine learning, optimization, and high-performance computing. He is a recipient of the U.S. Department of Energy 2018 Early Career Award. Prior to Argonne, he worked as a Chief Technology Officer at Mentis Sprl, a machine learning startup in Brussels, Belgium. He received his PhD from CoDE-IRIDIA (AI Lab), Università Libre de Bruxelles, Brussels, Belgium, where he was a recipient of European Commissions Marie Curie and Belgian F.R.S-FNRS Aspirant fellowships.

Presenter Website: http://pbalapra.github.io/
Department Link: https://deephyper.readthedocs.io/en/latest/