In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
While talking, people may move heavily their arms around, remain expressionless, or even display subtle facial movements... These differences may arise from personality, cultural,...
In this paper we present a design space exploration flow to achieve energy efficiency for streaming applications on MPSoCs while meeting the specified throughput constraints. The ...
We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of a...
We consider how to efficiently allocate computing resources in order to infer the best of a finite set of simulated systems, where best means that the system has the maximal expec...