We pose the problem of perfect segmentation for regions with ambiguous boundaries. We design machine learning classifiers to identify boundaries and build these into an interactiv...
We introduce a new inference algorithm for Dirichlet process mixture models. While Gibbs sampling and variational methods focus on local moves, the new algorithm makes more global...
Causal analysis of continuous-valued variables typically uses either autoregressive models or linear Gaussian Bayesian networks with instantaneous effects. Estimation of Gaussian ...
Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear supp...
We describe a generative model of the relationship between two images. The model is defined as a factored threeway Boltzmann machine, in which hidden variables collaborate to de...
Joshua Susskind, Roland Memisevic, Geoffrey Hinton...