This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...
Recent research in machine learning has focused on breaking audio spectrograms into separate sources of sound using latent variable decompositions. These methods require that the ...
The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a ...
Allan Tucker, Peter A. C. 't Hoen, Veronica Vincio...
Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for...
Giannis K. Chantas, Nikolas P. Galatsanos, Aristid...
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...