Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different for...
Traditional minimum area retiming algorithms attempt to achieve their prescribed objective with no regard to maintaining the initial state of the system. This issue is important f...
Abstract--Reinforcement learning (RL) research typically develops algorithms for helping an RL agent best achieve its goals-however they came to be defined--while ignoring the rela...
Abstract Abstract. Recent work in the analysis of randomized approximation algorithms for NP-hard optimization problems has involved approximating the solution to a problem by the ...
We present a system-level approach for power optimization under a set of user specified costs and timing constraints of hard real-time designs. The approach optimizes all three d...