Feedback-directed optimization (FDO) is effective in improving application runtime performance, but has not been widely adopted due to the tedious dual-compilation model, the dif...
Dehao Chen, Neil Vachharajani, Robert Hundt, Shih-...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
We apply a scalable approach for practical, comprehensive design space evaluation and optimization. This approach combines design space sampling and statistical inference to ident...
The paper discusses computationally efficient NLMS and RLS algorithms for a broad class of nonlinear filters using periodic input sequences. The class comprises all nonlinear ...
Alberto Carini, V. John Mathews, Giovanni L. Sicur...
We study functions with multiple output values, and use active sampling to identify an example for each of the possible output values. Our results for this setting include: (1) Eff...