Designing custom solutions has been central to meeting a range of stringent and specialized needs of embedded computing, along such dimensions as physical size, power consumption, ...
Krishna V. Palem, Lakshmi N. Chakrapani, Sudhakar ...
We present a methodology for microarchitectural customization of embedded processors by exploiting application information, thus attaining the twin benefits of processor standardi...
Universal kernels have been shown to play an important role in the achievability of the Bayes risk by many kernel-based algorithms that include binary classification, regression, ...
Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. ...
This paper proposes a convolution forest kernel to effectively explore rich structured features embedded in a packed parse forest. As opposed to the convolution tree kernel, the p...
In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a ...