The growing complexity of systems and their implementation into silicon encourages designers to look for model designs at higher levels of abstraction and then incrementally build ...
—In this paper we address the problem of scalable and load balanced routing for wireless sensor networks. Motivated by the analog of the continuous setting that geodesic routing ...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
—This paper improves on the best-known runtime and measurement bounds for a recently proposed Deterministic sublinear-time Sparse Fourier Transform algorithm (hereafter called DS...
We give a O( log n)-approximation algorithm for sparsest cut, edge expansion, balanced separator, and graph conductance problems. This improves the O(log n)-approximation of Leig...