The difficulty of handling out-of-core data limits the performance of supercomputers as well as the potential of the parallel machines. Since writing an efficient out-of-core ve...
Mahmut T. Kandemir, Alok N. Choudhary, J. Ramanuja...
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
Abstract. Over the past few years, virtualization has been employed to environments ranging from densely populated cloud computing clusters to home desktop computers. Security rese...
Abstract--Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a nov...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T...
Abstract--We study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic...