We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
The massive distribution of the crawling task can lead to inefficient exploration of the same portion of the Web. We propose a technique to guide crawlers exploration based on the...
We seek to increase user confidence in simulations as they are adapted to meet new requirements. Our approach includes formal representation of uncertainty, lightweight validation,...
Paul F. Reynolds Jr., Michael Spiegel, Xinyu Liu, ...
This paper details the first step of the Design Trotter framework for design space exploration applied to dedicated SOCs. The aim of this step is to provide metrics in order to gu...
Yannick Le Moullec, Nahla Ben Amor, Jean-Philippe ...
The growing adoption of reconfigurable architectures opens new implementation alternatives and creates new design challenges. In the case of dynamically reconfigurable architectur...