The Machine Learning Hub is excited to present this upcoming seminar:
Reuse-Centric Programming System Support of Machine Learning
By Hui Guan, Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst
Monday, April 11, 2:00 p.m. – 3:00 p.m. in Lecture Hall between B4 and B5
Abstract
Modern machine learning, especially deep learning, faces a fundamental question: how to create models that efficiently deliver reliable predictions to meet the requirements of diverse applications running on various systems. This talk will introduce reuse-centric optimization, a novel direction for addressing the fundamental question. Reuse-centric optimization centers around harnessing reuse opportunities for enhancing computing efficiency. It generalizes the principle to a higher level and a larger scope through a synergy between programming systems and machine learning algorithms. Its exploitation of computation reuse spans across the boundaries of machine learning algorithms, implementations, and infrastructures; the types of reuse it covers range from pre-trained Neural Network building blocks to preprocessed results and even memory bits; the scopes of reuse it leverages go from training pipelines of deep learning to variants of Neural Networks in ensembles; the benefits it generates extend from orders of magnitude faster search for a good smaller Convolution Neural Network (CNN) to the elimination of all space cost in protecting parameters of CNNs. This talk will also cover our recent progress on extending the reuse-centric optimization to support deep multi-task learning.
About the speaker
Hui Guan is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, the flagship campus of the UMass system. She received her Ph.D. in Electrical Engineering from North Carolina State University in 2020. Her research lies in the intersection between Machine Learning and Programming Systems, with an emphasis on improving the speed, scalability and reliability of Machine Learning through innovations in algorithms and programming systems (e.g., compilers, runtime). She is also interested in leveraging Machine Learning to improve High Performance Computing. Her current research focuses on both algorithm and system optimizations of Deep Multi-Task Learning and Graph Machine Learning.