Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
We present a study on various statistics relevant to research on color constancy. Many of these analyses could not have been done before simply because a large database for color ...
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
Modularity and symmetry are two properties observed in almost every engineering and biological structure. The origin of these properties in nature is still unknown. Yet, as engine...
We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...