Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Computational models of grounded language learning have been based on the premise that words and concepts are learned simultaneously. Given the mounting cognitive evidence for conc...
As new processor and memory architectures advance, clusters start to be built from larger SMP systems, which makes MPI intra-node communication a critical issue in high performanc...
Memory elements are the most vulnerable system component to soft errors. Since memory elements in cache arrays consume a large fraction of the die in modern microprocessors, the p...
Hossein Asadi, Vilas Sridharan, Mehdi Baradaran Ta...
— Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of ...
Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mo...