Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given da...
Charles Kemp, Joshua B. Tenenbaum, Thomas L. Griff...
We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three lay...
Nikita A. Sakhanenko, George F. Luger, Carl R. Ste...
This work casts the traffic analysis of anonymity systems, and in particular mix networks, in the context of Bayesian inference. A generative probabilistic model of mix network ar...
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely states of a set of variables given partial evidence on the complement of that set...
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is h...