The problem of learning metrics between structured data (strings, trees or graphs) has been the subject of various recent papers. With regard to the specific case of trees, some a...
Sequence data are abundant in application areas such as computational biology, environmental sciences, and telecommunications. Many real-life sequences have a strong segmental str...
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
We present a model of a `gas of circles', the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short ran...
Peter Horvath, Ian Jermyn, Zoltan Kato, and Josian...
We present a novel technique for image inpainting, the problem of filling-in missing image parts. Image inpainting is ill-posed and we adopt a probabilistic model-based approach ...