Efficient and Robust Machine Learning

Speaker:  Tianyi Zhou, ASTAR, Singapore.

Time:       10:00-11:00, April 14th

Location: Room 1A-200, SIST

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 provide introduce some machine learning techniques from my group facilitating these real-world requirements.

Bio: