We present a new domain for unsupervised learning: automatically customizing the computer to a specific melodic performer by merely listening to them improvise. We also describe B...
Abstract. Retrieving semistructured (XML) data typically requires either a structured query such as XPath, or a keyword query that does not take structure into account. In this pap...
We present a probabilistic model for a document corpus that combines many of the desirable features of previous models. The model is called “GaP” for Gamma-Poisson, the distri...
We propose the use of latent space models applied to local invariant features for object classification. We investigate whether using latent space models enables to learn patterns...
Florent Monay, Pedro Quelhas, Daniel Gatica-Perez,...
Abstract. We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information...