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ORCIDHélène Paugam-Moisy
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2020 – today
- 2020
[j8]Emmanuel Biabiany
, Didier Bernard, Vincent Pagé, Hélène Paugam-Moisy:
Design of an expert distance metric for climate clustering: The case of rainfall in the Lesser Antilles. Comput. Geosci. 145: 104612 (2020)- [i3]Emmanuel Biabiany, Vincent Pagé, Didier Bernard, Hélène Paugam-Moisy:
Using an expert deviation carrying the knowledge of climate data in usual clustering algorithms. CoRR abs/2006.05603 (2020)
2010 – 2019
- 2019
- [c32]Stéphane Cholet, Hélène Paugam-Moisy, Sébastien Régis:
Bidirectional Associative Memory for Multimodal Fusion : a Depression Evaluation Case Study. IJCNN 2019: 1-6 - 2018
- [c31]Stéphane Cholet, Hélène Paugam-Moisy:
Prototype-based Classifier for Automatic Diagnosis of Depressive Mood. ICBEA 2018: 1-6 - [c30]Stéphane Cholet, Hélène Paugam-Moisy:
Diagnostic automatique de l'état dépressif(Classification of depressive moods). CNIA+RJCIA 2018: 102-109 - 2014
- [p2]Sébastien Rebecchi, Hélène Paugam-Moisy, Michèle Sebag:
Learning Sparse Features with an Auto-Associator. Growing Adaptive Machines 2014: 139-158 - 2012
- [c29]Anthony Mouraud, Quentin Barthélemy, Aurélien Mayoue, Cédric Gouy-Pailler, Anthony Larue, Hélène Paugam-Moisy:
From neuronal cost-based metrics towards sparse coded signals classification. ESANN 2012 - [p1]Hélène Paugam-Moisy, Sander M. Bohté:
Computing with Spiking Neuron Networks. Handbook of Natural Computing 2012: 335-376 - 2011
- [c28]Sylvain Chevallier, Nicolas Bredèche, Hélène Paugam-Moisy, Michèle Sebag:
Emergence of temporal and spatial synchronous behaviors in a foraging swarm. ECAL 2011: 125-132 - [c27]Ludovic Arnold, Sébastien Rebecchi, Sylvain Chevallier, Hélène Paugam-Moisy:
An Introduction to Deep Learning. ESANN 2011 - 2010
- [c26]Ludovic Arnold, Hélène Paugam-Moisy, Michèle Sebag:
Unsupervised Layer-Wise Model Selection in Deep Neural Networks. ECAI 2010: 915-920 - [c25]Anthony Mouraud, Alain Guillaume, Hélène Paugam-Moisy:
The DAMNED Simulator for Implementing a Dynamic Model of the Network Controlling Saccadic Eye Movements. ICANN (1) 2010: 272-281 - [c24]Sylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag:
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. NIPS 2010: 379-387
2000 – 2009
- 2009
- [c23]Régis Martinez, Hélène Paugam-Moisy:
Algorithms for Structural and Dynamical Polychronous Groups Detection. ICANN (2) 2009: 75-84 - 2008
- [j7]Hélène Paugam-Moisy, Régis Martinez, Samy Bengio:
Delay learning and polychronization for reservoir computing. Neurocomputing 71(7-9): 1143-1158 (2008) - [c22]David Meunier, Hélène Paugam-Moisy:
Neural networks for computational neuroscience. ESANN 2008: 367-378 - 2007
- [c21]Hélène Paugam-Moisy, Régis Martinez, Samy Bengio:
A supervised learning approach based on STDP and polychronization in spiking neuron networks. ESANN 2007: 427-432 - 2006
- [c20]David Meunier, Hélène Paugam-Moisy:
Cluster detection algorithm in neural networks. ESANN 2006: 19-24 - [c19]Sylvain Chevallier, Philippe Tarroux, Hélène Paugam-Moisy:
Saliency extraction with a distributed spiking neural network. ESANN 2006: 209-214 - [c18]Anthony Mouraud, Hélène Paugam-Moisy:
Learning and discrimination through STDP in a top-down modulated associative memory. ESANN 2006: 611-616 - [c17]Anthony Mouraud, Hélène Paugam-Moisy, Didier Puzenat:
A Distributed and Multithreaded Neural Event Driven Simulation Framework. Parallel and Distributed Computing and Networks 2006: 212-217 - [i2]Anthony Mouraud, Hélène Paugam-Moisy:
Learning and discrimination through STDP in a top-down modulated associative memory. CoRR abs/cs/0611104 (2006) - 2005
- [j6]David Meunier, Hélène Paugam-Moisy:
Simulation d'un amorçage intermodal sur un réseau de neurones impulsionnels. Rev. d'Intelligence Artif. 19(1-2): 375-388 (2005) - [c16]Sylvain Chevallier, Hélène Paugam-Moisy, François Lemaître:
Distributed Processing for Modelling Real-Time Multimodal Perception in a Virtual Robot. Parallel and Distributed Computing and Networks 2005: 393-398 - [i1]Anthony Mouraud, Didier Puzenat, Hélène Paugam-Moisy:
DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework. CoRR abs/cs/0512018 (2005) - 2004
- [j5]Yann Guermeur, Gianluca Pollastri, André Elisseeff, Dominique Zelus, Hélène Paugam-Moisy, Pierre Baldi:
Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Neurocomputing 56: 305-327 (2004) - [c15]David Meunier, Hélène Paugam-Moisy:
A "spiking" bidirectional associative memory for modeling intermodal priming. Neural Networks and Computational Intelligence 2004: 25-30 - 2002
- [j4]Pablo A. Estévez, Hélène Paugam-Moisy, Didier Puzenat, Manuel Ugarte:
A scalable parallel algorithm for training a hierarchical mixture of neural experts. Parallel Comput. 28(6): 861-891 (2002) - [c14]Hélène Paugam-Moisy, Didier Puzenat, Emanuelle Reynaud, Jean-Philippe Magué:
Neural networks for modelling memory : case studies. ESANN 2002: 71-82 - 2001
- [c13]Olivier Teytaud, Hélène Paugam-Moisy:
Bounds on the Generalization Ability of Bayesian Inference and Gibbs Algorithms. ICANN 2001: 265-270 - 2000
- [c12]Hélène Paugam-Moisy, André Elisseeff, Yann Guermeur:
Generalization Performance of Multiclass Discriminant Models. IJCNN (4) 2000: 177-182 - [c11]Yann Guermeur, André Elisseeff, Hélène Paugam-Moisy:
A New Multi-Class SVM Based on a Uniform Convergence Result. IJCNN (4) 2000: 183-188
1990 – 1999
- 1999
- [j3]André Elisseeff, Hélène Paugam-Moisy:
JNN, a randomized algorithm for training multilayer networks in polynomial time. Neurocomputing 29(1-3): 3-24 (1999) - [c10]Cédric Bertolini, Hélène Paugam-Moisy, Didier Puzenat:
Priming an Artificial Associative Memory. IWANN (1) 1999: 348-356 - 1998
- [j2]Claire Kenyon, Hélène Paugam-Moisy:
Multilayer Neural Networks and Polyhedral Dichotomies. Ann. Math. Artif. Intell. 24(1-4): 115-128 (1998) - 1996
- [c9]V. Demian, Frederic Desprez, Hélène Paugam-Moisy, Makan Pourzandi:
Parallel Implementation of RBF Neural Networks. Euro-Par, Vol. II 1996: 243-250 - [c8]Graham R. Brightwell, Claire Kenyon, Hélène Paugam-Moisy:
Multilayer Neural Networks: One or Two Hidden Layers? NIPS 1996: 148-154 - [c7]André Elisseeff, Hélène Paugam-Moisy:
Size of Multilayer Networks for Exact Learning: Analytic Approach. NIPS 1996: 162-168 - 1995
- [c6]Bernard Girau, Hélène Paugam-Moisy:
Load sharing in the training set partition algorithm for parallel neural learning. IPPS 1995: 586-591 - 1994
- [j1]Michel Cosnard, Pascal Koiran, Hélène Paugam-Moisy:
Bounds on the Number of Units for Computing Arbitrary Dichotomies by Multilayer Perceptrons. J. Complex. 10(1): 57-63 (1994) - [c5]Arnulfo P. Azcarraga, Hélène Paugam-Moisy, Didier Puzenat:
A Incremental Neural Classifier on a MIMD Parallel Computer. Applications in Parallel and Distributed Computing 1994: 13-22 - [c4]D. Girard, Hélène Paugam-Moisy:
Strategies of Weight Updating for Parallel Back-propagation. Applications in Parallel and Distributed Computing 1994: 335-336 - 1992
- [c3]Hélène Paugam-Moisy:
Optimal Speedup Conditions for a Parallel Back-Propagation Algorithm. CONPAR 1992: 719-724 - [c2]S. Amghar, Hélène Paugam-Moisy, J. P. Royet:
Learning Methods for Odor Recognition Modeling. IPMU 1992: 361-367 - [c1]Michel Cosnard, Pascal Koiran, Hélène Paugam-Moisy:
Complexity Issues in Neural Network Computations. LATIN 1992: 530-543
Coauthor Index
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