Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
— To optimize makespan and reliability for workflow applications, most existing works use list heuristics rather than genetic algorithms (GAs) which can usually give better solut...
Cost pressure is driving vendors of safety-critical systems to integrate previously distributed systems. One natural approach we have previous introduced is On-Demand Redundancy (...
Brett H. Meyer, Benton H. Calhoun, John Lach, Kevi...
Scalable similarity search is the core of many large scale learning or data mining applications. Recently, many research results demonstrate that one promising approach is creatin...
Implementing real-time video processing systems put high requirements on computation and memory performance. FPGAs have proven to be effective implementation architecture for thes...