—We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation t...
— We present a continuous, stochastic model — the first of its kind — for the monetary flow in multi-level markets allowing for the quantitative assessment of the incentive...
This paper presents an unsupervised learning approach to video-based face recognition that does not make any assumptions about the pose, expressions or prior localization of landm...
This paper describes Slepian-Wolf codes based on overlapped quasi-arithmetic codes, where overlapping allows lossy compression of the source below its entropy. In the context of s...
Xavier Artigas, Simon Malinowski, Christine Guille...
Abstract. We advocate to analyze the average complexity of learning problems. An appropriate framework for this purpose is introduced. Based on it we consider the problem of learni...