Bayesian Optimization and Embedded Learning Systems
published: Sept. 5, 2016, recorded: August 2016, views: 1808
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
An important property of embedded learning systems is the ever-changing environment they create for all algorithms operating in the system. Optimizing the performance of those algorithms becomes a perpetual on-line activity rather than a one-off task. I will review some of these challenges in autonomous vehicles. I will discuss Bayesian optimization methods and their application in robotics and scientific applications, focusing on scaling up the dimensionality and managing multi-fidelity evaluations. I will finish with lessons learned and thoughts on future directions as these methods move into embedded systems.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !