activity
20112017
most citedA deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series

615 citations · 661 across the 9 of their papers we have counts for

collaborators

10 papers

stat.ML2017★ 615 cited

A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series

Stanislas Chambon, Mathieu Galtier, Pierrick Arnal +2

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of…

physics.comp-ph2016★ 31 cited

Addressing nonlinearities in Monte Carlo

Jérémi Dauchet, Jean-Jacques Bezian, Stéphane Blanco +16

Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has sever…

nlin.AO2014

Ideomotor feedback control in a recurrent neural network

Mathieu Galtier

The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and ac…

nlin.CD2014★ 3 cited

Regular graphs maximize the variability of random neural networks

Gilles Wainrib, Mathieu Galtier

In this work we study the dynamics of systems composed of numerous interacting elements interconnected through a random weighted directed graph, such as models of random neural net…

nlin.CD2014★ 3 cited

A local Echo State Property through the largest Lyapunov exponent

Mathieu Galtier, Gilles Wainrib

Echo State Networks are efficient time-series predictors, which highly depend on the value of the spectral radius of the reservoir connectivity matrix. Based on recent results on t…

nlin.AO2014★ 1 cited

Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes

Mathieu N. Galtier, Camille Marini, Gilles Wainrib +1

A method is provided for designing and training noise-driven recurrent neural networks as models of stochastic processes. The method unifies and generalizes two known separate mode…