We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Abstract. The epsilon-inflation proved to be useful and necessary in many verification algorithms. Different definitions of an epsilon-inflation are possible, depending on the...
This paper proposes a low-power high-throughput digital signal processor (DSP) for baseband processing in wireless terminals. It builds on our earlier architecture--Signal processi...
Hyunseok Lee, Chaitali Chakrabarti, Trevor N. Mudg...
This paper presents an automated performance tuning solution, which partitions a program into a number of tuning sections and finds the best combination of compiler options for e...
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...