University of California, San Diego, USA
Title: M2L: Bringing the Machine into the loop of Machine Learning
Abstract: Contemporary massive data analytical algorithms are often focused on functionality and accuracy with system performance as an afterthought. As their use/scale grows and the computing platforms become diverse, spanning from servers and desktops to smartphones and Internet of Things (IoT) devices, functionality is not just about algorithmic efficiency and accuracy, but also practicality on real-world computing machines. One-size fits all solutions will not meet the physical needs of emerging massive data analytic application scenarios. In this talk, I will present our research on novel automated computing frameworks that bring hardware into the loop of designing scalable inference algorithms and learning systems, supported by both theoretical and practical results. Proof-of-concept evaluations on diverse datasets, applications, algorithms, and