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Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in music. In this paper, a distributed representation for chords is designed su ...
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 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 ...
In this paper, we show that the hinge loss can be interpreted as the neg-log-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semi-parametric model fitted by a maximum a ...
We present a common probabilistic framework for kernel or spline smooth- ing methods, including popular architectures such as Gaussian processes and Support Vector machines. We identify the problem of unnormalized loss func- tions and suggest a general tec ...
Many applications, such as the dissemination of stock quote events, require reliable and totally ordered delivery of broadcast messages to a large number of processes. Recently, gossip-based broadcast algorithms have been praised as an interesting alternat ...
In this paper, we show that the hinge loss can be interpreted as the neg-log-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semi-parametric model fitted by a maximum a ...
Resource location is a fundamental problem for large-scale distributed applications. This paper discusses the problem from a probabilistic perspective. Contrary to deterministic approaches, which strive to produce a precise outcome, probabilistic approache ...
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 ...
