Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...
We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We...
David M. Blei, Thomas L. Griffiths, Michael I. Jor...
A key phenomenon in the dynamics of genetic networks is the cell cycle. In the study of this phenomenon, an important task is to understand how many processes, acting on different...
Xilin Wei, Roderick V. N. Melnik, Gabriel Moreno-H...
During the development of car engines, regression models that are based on machine learning techniques are increasingly important for tasks which require a prediction of results i...
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent v...