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.
Visual SLAM and Structure from Motion in Dynamic Environments: A Survey
Muhamad Risqi U. Saputra Andrew Markham and Niki Trigoni
In ACM Computing Surveys. Vol. 51‚ Issue 2. 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.
DeepVO: Towards End−to−End Visual Odometry with Deep Recurrent Convolutional Neural Networks
S. Wang‚ R. Clark‚ H. Wen and N. Trigoni
In International Conference on Robotics and Automation. 2017.