Presentation
Invited: Lightweight Uncertainty Quantification at the Edge: Know When Your AI model Doesn't
DescriptionDeep neural networks (DNNs), crucial in fields like autonomous vehicles, often lack the necessary transparency and reliability. This talk addresses these limitations by introducing conformal prediction as an efficient alternative to the computationally intensive Bayesian inference, particularly suited for edge computing. We focus on extracting predictive uncertainties in applications such as visual odometry (VO) and 3D object detection. Our approach involves novel loss functions and training methods that utilize mutual information from various sensor streams and are capable of predicting disjoint uncertainty bounds. Additionally, we explore the interaction of sensing noise with predictive uncertainties, offering a dynamic, information-theoretic approach to regulate sensor power in real-time applications.
Event Type
Special Session (Research)
TimeWednesday, June 2611:30am - 12:00pm PDT
Location3006, 3rd Floor
AI
Design