Belén Heredia, María Prieur, Clémentine Eckert, Nicolas

Avalanche models are increasingly employed for elaborating land-use maps and designing defense structures, but they rely on poorly known parameters. Careful uncertainty assessment is thus required but difficulty arises from the nature of the outputs of these models, which are commonly both functional and scalar. Hence, so far in the avalanche field...

Gamboa, Fabrice Klein, Thierry Lagnoux, Agnès Moreno, Leonardo

Divol, Vincent Lacombe, Théo

Persistence diagrams (PDs) are the most common descriptors used to encode the topology of structured data appearing in challenging learning tasks; think e.g. of graphs, time series or point clouds sampled close to a manifold. Given random objects and the corresponding distribution of PDs, one may want to build a statistical summary-such as a mean-o...

Biernacki, Christophe Vandewalle, Vincent

International audience

Lavancier, Frédéric Le Guével, Ronan

Spatial birth-death processes are generalisations of simple birth-death processes, where the birth and death dynamics depend on the spatial locations of individuals. In this article, we further let individuals move during their life time according to a continuous Markov process. This generalisation, that we call a spatial birth-death-move process, ...

Marrelec, Guillaume Giron, Alain
Published in
IEEE transactions on pattern analysis and machine intelligence

Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, usually under the asymptotic a...

Atto, Abdourrahmane M Bisset, Rosie R Trouve, Emmanuel
Published in
IEEE transactions on neural networks and learning systems

This brief addresses understandability of modern machine learning networks with respect to the statistical properties of their convolution layers. It proposes a set of tools for categorizing a convolution layer in terms of kernel property (meanlet, differencelet, or distrotlet) or kernel sequence property (frame spectra and intralayer correlation m...

Allassonnière, Stéphanie Chevallier, Juliette

The expectation-maximization (EM) algorithm is a powerful computational technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed form, a stochastic approximation of EM (SAEM) can be used. The convergence of the SAEM toward local maxima of the observed likelihood has been proved a...

Rosuel, Alexis Loubaton, Philippe Vallet, Pascal Mestre, Xavier

This paper analyzes the detection of a M-dimensional useful signal modeled as the output of a M ×K MIMO filter driven by a K-dimensional white Gaussian noise, and corrupted by a M-dimensional Gaussian noise with mutually uncorrelated components. The study is focused on frequency domain test statistics based on the eigenvalues of an estimate of the ...

Rosuel, Alexis Loubaton, Philippe Vallet, Pascal

We investigate the asymptotic distribution of the maximum of a frequency smoothed estimate of the spectral coherence of a M-variate complex Gaussian time series with mutually independent components when the dimension M and the number of samples N both converge to infinity. If B denotes the smoothing span of the underlying smoothed periodogram estim...