Project Team Tao

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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Section: New Results

Large and Deep Networks

Participants : Ludovic Arnold, Sylvain Chevallier, Anthony Mouraud, Hélène Paugam-Moisy, Sébastien Rebecchi, Michèle Sebag.

Deep networks: RBM or AA

The two main families of deep networks are implemented and studied by TAO: stacked RBM (Restricted Boltzmann Machines) and stacked AA (Auto-Associators).

Learning sparse features for deep networks

Inspired by the theory of compressed sensing and beyond the common methods based on dictionary learning, we have proposed to learn sparsity and accuracy simultaneously by alternating two constraints on the weights of an Auto-Associator [55] .

Spiking neuron networks for swarm robotics

The model "SpikeAnts" [91] has been applied to a spatial robotic environment [23] , in collaboration with Nicolas Bredeche (see Section 6.2 , and has demonstrated even more its interest in the context of swarm robotics.