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We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate B ...
We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate B ...
In our previous work, tracking the iso-level sets through total variation scale-space proved to be a very efficient tool for unsupervised segmentation. Stepping on these results, we propose a new segmentation approach in a unified total variation framework ...
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic model, i.e., where links lose messages and where processes crash and recover probabilistically. Our approach consists in first defining the optimality ...
In this paper, we introduce probabilistic framework for robust identification of the user goals in human-robot speech-based interaction. The concept of Bayesian networks is used for interpreting multimodal signals in the spoken dialogue between a tour-guid ...
This paper proposes an approach allowing topology learning and recognition in indoor environments by using a probabilistic approach called Bayesian Programming. The main goal of this approach is to cope with the uncertainty, imprecision and incompleteness ...
In this paper, we introduce Bayesian networks architecture for combining speech-based information with that from another modality for error handling in human-robot dialogue system. In particular, we report on experiments interpreting speech and laser scann ...
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binar ...
Department of Statistics, University of Berkeley, CA2004
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic unreliable model. Our approach consists in first defining the optimality of probabilistic reliable broadcast algorithms and the adaptiveness of algorit ...