Topic: Efficient and Robust Machine Learning
Speaker: Dr. ZHOU Tianyi, Senior Scientist, Institute of High Performance Computing (IHPC) of A*STAR, Singapore
Date and time: 10:00–11:00, April 14
Venue: Room 1A-200, SIST
Host: GAO Shenghua
Abstract:
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being ‘aware’ of its limits, i.e. the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day’s applications. In this talk, we will introduce some machine learning techniques from our group to facilitate these real-world requirements.
Biography:
ZHOU Tianyi is currently a senior scientist, investigator and group manager with A*STAR’s Centre for Frontier AI Research (CFAR), Singapore. He also holds an adjunct faculty position at the National University of Singapore (NUS). Before he joined IHPC, he was a senior research engineer with SONY US Research Center in San Jose, USA. ZHOU received his PhD in computer science from Nanyang Technological University (NTU), Singapore. His current interests focus mainly on machine learning with limited resources and their applications to natural language processing and computer vision tasks.