Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor fee...
Michael L. Littman, Anthony R. Cassandra, Leslie P...
In this paper we describe how Network-on-Chip (NoC) will be the next major challenge to implementing complex and function-rich applications in advanced manufacturing processes at ...
— Process variations cause significant timing uncertainty and yield degradation in deep sub-micron technologies. A solution to counter timing uncertainty is post-silicon clock t...
We present an automatic and efficient method to extract spatio-temporal human volumes from video, which combines top-down model-based and bottom-up appearancebased approaches. Fr...