Machine Learning Systems
Designing systems that are largely defined by the execution of machine learning workloads present new open problems that straddle the domains of systems, hardware and artificial intelligence. Cyber-physical systems are a prime example of this emerging category. This activity considers the needs of the next generation of machine-learning-centric systems in terms of: design, interfaces and abstractions; parallel, distributed and scalable learning/inference algorithms; hardware co-design for efficiency and high-utilization; and finally, interpretability, security and testing.
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Chris Xiaoxuan Lu‚ Xuan Kan‚ Bowen Du‚ Changhao Chen‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni and John Stankovic
In The Web Conference (WWW). 2019.
Demo Abstract: Automatic Face Recognition Adaptation via Ambient Wireless Identifiers
Chris Xiaoxuan Lu ‚ Peijun Zhao‚ Bowen Du‚ Hongkai Wen‚ Andrew Markham‚ Stefano Rosa and Niki Trignoni
In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems (SenSys). 2018.
VINet: Visual Inertial Odometry as a Sequence to Sequence Learning Problem
R. Clark‚ S. Wang‚ H. Wen‚ A. Markham and N. Trigoni
In AAAI Conference on Artificial Intelligence (AAAI). 2017.