To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
- In this paper, we study the algorithm design aspects of three newly developed spin-wave architectures. The architectures are capable of simultaneously transmitting multiple signa...
We present the path sequence storage model, a new logical model for storing XML documents. This model partitions XML data and content according to the document paths; and uses orde...
Ioana Manolescu, Andrei Arion, Angela Bonifati, An...
Neither simulation results nor real system results give an explanation to the behavior of advanced computer systems for the full design spectrum. In this paper, we present simple ...