Embedding Knowledge into Learning￼
Machine learning, particularly deep learning, has been spectacularly successful in the recent past. Human-level performance has been achieved in tasks such as object recognition and speech recognition. However, these successes are achieved for highly constrained domains that requires minimal amounts of reasoning and planning, and use large amounts of labeled training data.
This work aims to incorporate knowledge about model structures, constraints, prior knowledge, as well as inference and planning algorithms into the machine learning methods in order to extend their reach on problems that require reasoning and planning. In particular, we aim develop methods that combine deep learning methods with probabilistic graphical models, in order to exploit the strengths of both methods, enabling models to be trained with less training data, and scaling up machine learning to work on more complex problems.
Research Technical Area
- Machine learning
- Reasoning under uncertainty
- Planning and scheduling
Benefits to the society
The software implementing the techniques developed by the project team is intended to be released as open-source software. This will allow others to build on the team’s work, accelerating progress in technology and generally benefitting society. The students and researchers trained will also benefit Singapore in terms of training manpower for the area of machine learning and AI.
Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier:
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis. ACL (1) 2019: 504-515
Zhen Zhang, Wee Sun Lee:
Deep Graphical Feature Learning for the Feature Matching Problem. International Conference on Computer Vision 2019. 2019
Team’s Principal Investigator
Professor Lee Wee Sun
School of Computing
National University of Singapore
Lee Wee Sun is a Professor in the Computer Science Department of School of Computing at the National University of Singapore. He obtained his Bachelor of Engineering (Hon I) in Computer Systems Engineering from the University of Queensland in 1992 and his Ph.D. from the Department of Systems Engineering at the Australian National University in 1996. His research interests include machine learning and planning under uncertainty.
Recent Notable Awards
- RoboCup Best Paper Award, International Conference on Intelligent Robots and Systems (IROS), 2015
- First Place, Humanitarian Robotics and Automation Technology Challenge (HRATC), 2015
- First Place, POMDP track, ICAPS International Probabilistic Planning Competition (IPPC) in 2011 and again in 2014
Prof. David Hsu, National University of Singapore Collaborator Prof. Simon See, NVIDIA AI Technology Centre
Research Focus: Machine learning, Reasoning under uncertainty, Robotics
Prof. David Hsu, National University of Singapore
Prof. Simon See, NVIDIA AI Technology Centre