A scalable parallel algorithm has been designed to perform multimillion-atom molecular dynamics (MD) simulations, in which first principlesbased reactive force fields (ReaxFF) des...
Ken-ichi Nomura, Rajiv K. Kalia, Aiichiro Nakano, ...
Creating replicas of frequently accessed objects across a read-intensive network can result in large bandwidth savings which, in turn, can lead to reduction in user response time....
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
In this paper we describe the parallelization of two nearest neighbour classification algorithms. Nearest neighbour methods are well-known machine learning techniques. They have be...
The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few...