Optimal Speedup Conditions for a Parallel Back-Propagation Algorithm. - Hélène Paugam-Moisy (CONPAR, 1992)

Learning Methods for Odor Recognition Modeling. - S. Amghar; Hélène Paugam-Moisy; J. P. Royet (IPMU, 1992)

Complexity Issues in Neural Network Computations. - Michel Cosnard; Pascal Koiran; Hélène Paugam-Moisy (LATIN, 1992)

A Incremental Neural Classifier on a MIMD Parallel Computer. - Arnulfo P. Azcarraga; Hélène Paugam-Moisy; Didier Puzenat (Applications in Parallel and Distributed Computing, 1994)

Strategies of Weight Updating for Parallel Back-propagation. - D. Girard; Hélène Paugam-Moisy (Applications in Parallel and Distributed Computing, 1994)

Bounds on the Number of Units for Computing Arbitrary Dichotomies by Multilayer Perceptrons. - Michel Cosnard; Pascal Koiran; Hélène Paugam-Moisy (J. Complex., 1994)

Load sharing in the training set partition algorithm for parallel neural learning. - Bernard Girau; Hélène Paugam-Moisy (IPPS, 1995)

Parallel Implementation of RBF Neural Networks. - V. Demian; Frederic Desprez; Hélène Paugam-Moisy; Makan Pourzandi (Euro-Par, Vol. II, 1996)

Multilayer Neural Networks: One or Two Hidden Layers? - Graham R. Brightwell; Claire Kenyon; Hélène Paugam-Moisy (NIPS, 1996)

Size of Multilayer Networks for Exact Learning: Analytic Approach. - André Elisseeff; Hélène Paugam-Moisy (NIPS, 1996)

Multilayer Neural Networks and Polyhedral Dichotomies. - Claire Kenyon; Hélène Paugam-Moisy (Ann. Math. Artif. Intell., 1998)

Priming an Artificial Associative Memory. - Cédric Bertolini; Hélène Paugam-Moisy; Didier Puzenat (IWANN (1), 1999)

JNN, a randomized algorithm for training multilayer networks in polynomial time. - André Elisseeff; Hélène Paugam-Moisy (Neurocomputing, 1999)

A New Multi-Class SVM Based on a Uniform Convergence Result. - Yann Guermeur; André Elisseeff; Hélène Paugam-Moisy (IJCNN (4), 2000)

Generalization Performance of Multiclass Discriminant Models. - Hélène Paugam-Moisy; André Elisseeff; Yann Guermeur (IJCNN (4), 2000)

Bounds on the Generalization Ability of Bayesian Inference and Gibbs Algorithms. - Olivier Teytaud; Hélène Paugam-Moisy (ICANN, 2001)

Neural networks for modelling memory : case studies. - Hélène Paugam-Moisy; Didier Puzenat; Emanuelle Reynaud; Jean-Philippe Magué (ESANN, 2002)

A scalable parallel algorithm for training a hierarchical mixture of neural experts. - Pablo A. Estévez; Hélène Paugam-Moisy; Didier Puzenat; Manuel Ugarte (Parallel Comput., 2002)

A "spiking" bidirectional associative memory for modeling intermodal priming. - David Meunier; Hélène Paugam-Moisy (Neural Networks and Computational Intelligence, 2004)

Combining protein secondary structure prediction models with ensemble methods of optimal complexity. - Yann Guermeur; Gianluca Pollastri; André Elisseeff; Dominique Zelus; Hélène Paugam-Moisy; Pierre Baldi (Neurocomputing, 2004)

Evolutionary supervision of a dynamical neural network allows learning with on-going weights. - David Meunier; Hélène Paugam-Moisy (IJCNN, 2005)

A multiple BAM for hetero-association and multisensory integration modelling. - Emanuelle Reynaud; Hélène Paugam-Moisy (IJCNN, 2005)

Distributed Processing for Modelling Real-Time Multimodal Perception in a Virtual Robot. - Sylvain Chevallier; Hélène Paugam-Moisy; François Lemaître (Parallel and Distributed Computing and Networks, 2005)

DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework - Anthony Mouraud; Didier Puzenat; Hélène Paugam-Moisy (CoRR, 2005)

Simulation d'un amorçage intermodal sur un réseau de neurones impulsionnels. - David Meunier; Hélène Paugam-Moisy (Rev. d'Intelligence Artif., 2005)

Saliency extraction with a distributed spiking neural network. - Sylvain Chevallier; Philippe Tarroux; Hélène Paugam-Moisy (ESANN, 2006)

Cluster detection algorithm in neural networks. - David Meunier; Hélène Paugam-Moisy (ESANN, 2006)

Learning and discrimination through STDP in a top-down modulated associative memory. - Anthony Mouraud; Hélène Paugam-Moisy (ESANN, 2006)

A Distributed and Multithreaded Neural Event Driven Simulation Framework. - Anthony Mouraud; Hélène Paugam-Moisy; Didier Puzenat (Parallel and Distributed Computing and Networks, 2006)

A supervised learning approach based on STDP and polychronization in spiking neuron networks. - Hélène Paugam-Moisy; Régis Martinez; Samy Bengio (ESANN, 2007)

Neural networks for computational neuroscience. - David Meunier; Hélène Paugam-Moisy (ESANN, 2008)

Delay learning and polychronization for reservoir computing. - Hélène Paugam-Moisy; Régis Martinez; Samy Bengio (Neurocomputing, 2008)

Algorithms for Structural and Dynamical Polychronous Groups Detection. - Régis Martinez; Hélène Paugam-Moisy (ICANN (2), 2009)

Unsupervised Layer-Wise Model Selection in Deep Neural Networks. - Ludovic Arnold; Hélène Paugam-Moisy; Michèle Sebag (ECAI, 2010)

The DAMNED Simulator for Implementing a Dynamic Model of the Network Controlling Saccadic Eye Movements. - Anthony Mouraud; Alain Guillaume; Hélène Paugam-Moisy (ICANN (1), 2010)

SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. - Sylvain Chevallier; Hélène Paugam-Moisy; Michèle Sebag (NIPS, 2010)

Emergence of temporal and spatial synchronous behaviors in a foraging swarm. - Sylvain Chevallier; Nicolas Bredèche; Hélène Paugam-Moisy; Michèle Sebag (ECAL, 2011)

An Introduction to Deep Learning. - Ludovic Arnold; Sébastien Rebecchi; Sylvain Chevallier; Hélène Paugam-Moisy (ESANN, 2011)

From neuronal cost-based metrics towards sparse coded signals classification. - Anthony Mouraud; Quentin Barthélemy; Aurélien Mayoue; Cédric Gouy-Pailler; Anthony Larue; Hélène Paugam-Moisy (ESANN, 2012)

Computing with Spiking Neuron Networks. - Hélène Paugam-Moisy; Sander M. Bohté (Handbook of Natural Computing, 2012)

Learning Sparse Features with an Auto-Associator. - Sébastien Rebecchi; Hélène Paugam-Moisy; Michèle Sebag (Growing Adaptive Machines, 2014)

Prototype-based Classifier for Automatic Diagnosis of Depressive Mood. - Stéphane Cholet; Hélène Paugam-Moisy (ICBEA, 2018)

Diagnostic automatique de l'état dépressif(Classification of depressive moods). - Stéphane Cholet; Hélène Paugam-Moisy (CNIA+RJCIA, 2018)

Bidirectional Associative Memory for Multimodal Fusion : a Depression Evaluation Case Study. - Stéphane Cholet; Hélène Paugam-Moisy; Sébastien Régis (IJCNN, 2019)

Using an expert deviation carrying the knowledge of climate data in usual clustering algorithms. - Emmanuel Biabiany; Vincent Pagé; Didier Bernard; Hélène Paugam-Moisy (CoRR, 2020)

Design of an expert distance metric for climate clustering: The case of rainfall in the Lesser Antilles. - Emmanuel Biabiany; Didier Bernard; Vincent Pagé; Hélène Paugam-Moisy (Comput. Geosci., 2020)