We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin ...
Ali Hormati, Olivier Roy, Yue M. Lu, Martin Vetter...
In this paper we present methods for downsampling datasets defined on graphs (i.e., graph-signals) by extending downsampling results for traditional N-dimensional signals. In par...
—In this paper we present an Information Theoretic Estimator for the number of sources mutually disjoint in a linear mixing model. The approach follows the Minimum Description Le...
Abstract. Supervised classifiers require manually labeled training samples to classify unlabeled objects. Active Learning (AL) can be used to selectively label only “ambiguous...
This paper proposes a new parallel execution model where programmers augment a sequential program with pieces of code called serializers that dynamically map computational operati...
Matthew D. Allen, Srinath Sridharan, Gurindar S. S...