DSP architectures typically provide indirect addressing modes with auto-increment and decrement. In addition, indexing mode is not available, and there are usually few, if any, ge...
Stan Y. Liao, Srinivas Devadas, Kurt Keutzer, Stev...
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...
This paper addresses the question of allocating computational resources among a set of algorithms in order to achieve the best performance on a scheduling problem instance. Our pr...
We describe and evaluate a system for learning domainspecific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well ...
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...