The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
Abstract: For cost-effective realization of sizable realtime distributed computing application systems, significant advances in resource allocation are in critical needs. An advanc...
K. H. (Kane) Kim, Yuqing Li, Kee-Wook Rim, Eltefaa...
Data coming from complex simulation models reach easily dimensions much greater than available computational resources. Visualization of such data still represents the most intuit...
Linear algebra algorithms are fundamental to many computing applications. Modern GPUs are suited for many general purpose processing tasks and have emerged as inexpensive high per...