Hélène Paugam-Moisy - researchr alias

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Viewing Publication 1 - 41 from 41

2020

Design of an expert distance metric for climate clustering: The case of rainfall in the Lesser AntillesEmmanuel Biabiany, Didier Bernard, Vincent Pagé, Hélène Paugam-Moisy. gandc, 145:104612, 2020. [doi]

2019

Bidirectional Associative Memory for Multimodal Fusion : a Depression Evaluation Case StudyStéphane Cholet, Hélène Paugam-Moisy, Sébastien Régis. ijcnn 2019: 1-6 [doi]

2018

Prototype-based Classifier for Automatic Diagnosis of Depressive MoodStéphane Cholet, Hélène Paugam-Moisy. icbea 2018: 1-6 [doi]

Diagnostic automatique de l'état dépressif(Classification of depressive moods)Stéphane Cholet, Hélène Paugam-Moisy. rjcia 2018: 102-109 [doi]

2014

Learning Sparse Features with an Auto-AssociatorSébastien Rebecchi, Hélène Paugam-Moisy, Michèle Sebag. In Taras Kowaliw, Nicolas Bredeche, René Doursat, editors, Growing Adaptive Machines - Combining Development and Learning in Artificial Neural Networks. Volume 557 of Studies in Computational Intelligence, pages 139-158, Springer, 2014. [doi]

2012

From neuronal cost-based metrics towards sparse coded signals classificationAnthony Mouraud, Quentin Barthelemy, Aurélien Mayoue, Cédric Gouy-Pailler, Anthony Larue, Hélène Paugam-Moisy. esann 2012: [doi]

2011

An Introduction to Deep LearningLudovic Arnold, Sébastien Rebecchi, Sylvain Chevallier, Hélène Paugam-Moisy. esann 2011: [doi]

Emergence of temporal and spatial synchronous behaviors in a foraging swarmSylvain Chevallier, Nicolas Bredeche, Hélène Paugam-Moisy, Michèle Sebag. ecal 2011: 125-132 [doi]

2010

Unsupervised Layer-Wise Model Selection in Deep Neural NetworksLudovic Arnold, Hélène Paugam-Moisy, Michèle Sebag. ecai 2010: 915-920 [doi]

SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex systemSylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag. nips 2010: 379-387 [doi]

The DAMNED Simulator for Implementing a Dynamic Model of the Network Controlling Saccadic Eye MovementsAnthony Mouraud, Alain Guillaume, Hélène Paugam-Moisy. icann 2010: 272-281 [doi]

2009

Algorithms for Structural and Dynamical Polychronous Groups DetectionRégis Martinez, Hélène Paugam-Moisy. icann 2009: 75-84 [doi]

2008

Neural networks for computational neuroscienceDavid Meunier, Hélène Paugam-Moisy. esann 2008: 367-378 [doi]

Delay learning and polychronization for reservoir computingHélène Paugam-Moisy, Régis Martinez, Samy Bengio. ijon, 71(7-9):1143-1158, 2008. [doi]

2007

A supervised learning approach based on STDP and polychronization in spiking neuron networksHélène Paugam-Moisy, Régis Martinez, Samy Bengio. esann 2007: 427-432 [doi]

2006

2005

Distributed Processing for Modelling Real-Time Multimodal Perception in a Virtual RobotSylvain Chevallier, Hélène Paugam-Moisy, François Lemaître. pdcn 2005: 393-398

Simulation d un amorçage intermodal sur un réseau de neurones impulsionnelsDavid Meunier, Hélène Paugam-Moisy. ria, 19(1-2):375-388, 2005. [doi]

2004

Combining protein secondary structure prediction models with ensemble methods of optimal complexityYann Guermeur, Gianluca Pollastri, André Elisseeff, Dominique Zelus, Hélène Paugam-Moisy, Pierre Baldi. ijon, 56:305-327, 2004. [doi]

A spiking bidirectional associative memory for modeling intermodal primingDavid Meunier, Hélène Paugam-Moisy. pet 2004: 25-30

2002

A scalable parallel algorithm for training a hierarchical mixture of neural expertsPablo A. Estévez, Hélène Paugam-Moisy, Didier Puzenat, Manuel Ugarte. pc, 28(6):861-891, 2002.

Neural networks for modelling memory : case studiesHélène Paugam-Moisy, Didier Puzenat, Emanuelle Reynaud, Jean-Philippe Magué. esann 2002: 71-82

2001

Bounds on the Generalization Ability of Bayesian Inference and Gibbs AlgorithmsOlivier Teytaud, Hélène Paugam-Moisy. icann 2001: 265-270 [doi]

2000

A New Multi-Class SVM Based on a Uniform Convergence ResultYann Guermeur, André Elisseeff, Hélène Paugam-Moisy. ijcnn 2000: 183-188 [doi]

Generalization Performance of Multiclass Discriminant ModelsHélène Paugam-Moisy, André Elisseeff, Yann Guermeur. ijcnn 2000: 177-182 [doi]

1999

Priming an Artificial Associative MemoryCédric Bertolini, Hélène Paugam-Moisy, Didier Puzenat. iwann 1999: 348-356

JNN, a randomized algorithm for training multilayer networks in polynomial timeAndré Elisseeff, Hélène Paugam-Moisy. ijon, 29(1-3):3-24, 1999. [doi]

1998

Multilayer Neural Networks and Polyhedral DichotomiesClaire Kenyon, Hélène Paugam-Moisy. AMAI, 24(1-4):115-128, 1998.

1996

Multilayer Neural Networks: One or Two Hidden Layers?G. Brightwell, Claire Kenyon, Hélène Paugam-Moisy. nips 1997: 148-154 [doi]

Parallel Implementation of RBF Neural NetworksV. Demian, Frederic Desprez, Hélène Paugam-Moisy, Makan Pourzandi. europar 1996: 243-250

Size of Multilayer Networks for Exact Learning: Analytic ApproachAndré Elisseeff, Hélène Paugam-Moisy. nips 1997: 162-168 [doi]

1995

Load sharing in the training set partition algorithm for parallel neural learningBernard Girau, Hélène Paugam-Moisy. ipps 1995: 586-591 [doi]

1994

A Incremental Neural Classifier on a MIMD Parallel ComputerArnulfo P. Azcarraga, Hélène Paugam-Moisy, Didier Puzenat. apdc 1994: 13-22

Bounds on the Number of Units for Computing Arbitrary Dichotomies by Multilayer PerceptronsMichel Cosnard, Pascal Koiran, Hélène Paugam-Moisy. jc, 10(1):57-63, 1994. [doi]

Strategies of Weight Updating for Parallel Back-propagationD. Girard, Hélène Paugam-Moisy. apdc 1994: 335-336

1992

Learning Methods for Odor Recognition ModelingS. Amghar, Hélène Paugam-Moisy, J. P. Royet. ipmu 1993: 361-367

Complexity Issues in Neural Network ComputationsMichel Cosnard, Pascal Koiran, Hélène Paugam-Moisy. latin 1992: 530-543

Optimal Speedup Conditions for a Parallel Back-Propagation AlgorithmHélène Paugam-Moisy. conpar 1992: 719-724

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