Distributed shared objects are a well known approach to achieve independenceof the memory model for parallel programming. The illusion of shared (global) objects is a conabstracti...
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
We present information-theoretically secure schemes for sharing and modifying secrets among a dynamic swarm of computing devices. The schemes support an unlimited number of change...
Shlomi Dolev, Juan A. Garay, Niv Gilboa, Vladimir ...
Abstract. We define a novel, basic, unsupervised learning problem learning the the lowest density homogeneous hyperplane separator of an unknown probability distribution. This task...
A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be sufficient for classifi...