Hélène PaugamMoisy Computer
Science Professor, Université des Antilles Head of AID research team at LAMIA PointeàPitre, Guadeloupe, France Previously: Professor at Université Lyon 2  Lyon, France Research position at INRIA Saclay  ÎledeFrance TAO team, at Université ParisSud  Orsay, France 

S. Rebecchi, H. PaugamMoisy and M. Sebag. Learning sparse features with an autoassociator. In T. Kowaliw, R. Doursat and N. Bredeche, Eds, Growing Adaptive Machines, 139–158, Springer, 2014.
H. PaugamMoisy and S.M. Bohte. Computing with Spiking Neuron Networks. In G. Rozenberg and T.H.W. Bäck and J.N. Kok, Eds, Handbook of Natural Computing, 335–376. Springer, 2012.
Y. Guermeur et H. PaugamMoisy. Théorie de l’apprentissage de Vapnik et SVM, Support Vector Machines. Dans M. Sebban et G. Venturini, éditeurs, Apprentissage automatique, pages 109–138. Hermès: Paris, 1999.
H. PaugamMoisy. Multiprocessor simulation of neural networks. In M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 605–608. The MIT Press, 1995.
H. PaugamMoisy. Parallel neural computing based on network duplicating. In I. Pitas, editor, Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks, pages 305–340. John Wiley, 1993.
H. PaugamMoisy and A. Pétrowski. Parallel neural computing based on algebraic partitioning. In I. Pitas, editor, Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks, pages 259–304. John Wiley, 1993.
B. Cessac, H. PaugamMoisy and T. Viéville. Overview of facts
and issues about neural coding by spikes. Journal of Physiology:
Paris. (35 pages  to appear). 2009
H. PaugamMoisy, R. Martinez and S. Bengio. Delay learning and polychronization for reservoir computing. NeuroComputing, 71: 11431158, Elsevier, 2008.
Y. Guermeur, G. Pollastri, A. Elisseeff, D. Zelus, H. PaugamMoisy and P. Baldi. Combining Protein Secondary Structure Prediction Models with Ensemble Methods of Optimal Complexity. NeuroComputing, 56: 305–327, Elsevier, 2004.
J.P. Royet, O. Koenig, H. PaugamMoisy, D. Puzenat and J.L. Chasse. Levels of processing effects on a task of olfactory naming. Perceptual & Motor Skills, 98: 197213, 2004.
P. Estévez, H. PaugamMoisy, D. Puzenat and M. Ugarte. A scalable parallel algorithm for training a hierarchical mixture of experts. Parallel Computing, 28: 861891. Elsevier, 2002.
E. Reynaud, A. Crépet, H. PaugamMoisy and D. Puzenat. A computational model for binding sensory modalities. Abstract in Consciousness and Cognition, 9(2): 87–88. Academic Press, 2000.
A. Elisseeff and H. PaugamMoisy. JNN, a Randomized Algorithm for Training Multilayer Networks in Polynomial Time. NeuroComputing, 29: 3–24, Elsevier, 1999.
C. Kenyon and H. PaugamMoisy. Multilayer neural networks and polyhedral dichotomies. Annals of Mathematics and Artiﬁcial Intelligence, 24(14): 115–128, Baltzer, 1998.
J.P. Royet, H. PaugamMoisy, C. Rouby, A.D. Zighed, N. Nicoloyannis, S. Amghar, and G. Sicard. Is shortterm odour recognition predictable from odour proﬁle ? Chemical Senses, 21 :553–566, Oxford University Press, 1996.
M. Cosnard, P. Koiran, and H. PaugamMoisy. Bounds on the number of
units for computing arbitrary dichotomies by multilayer perceptrons.
Journal of Complexity, 10: 57–63, Academic Press, 1994.
Mouraud, A. and Barthelemy, Q. and Mayoue, A. and GouyPailler, C. and Larue, A. and PaugamMoisy, H. From neuronal costbased metrics towards sparse coded signal classification. In Proc of ESANN’2012, M. Verleysen Ed., 311–316. Bruges, Belgium, April 2012.
Chevallier, S. and Bredeche, N. and PaugamMoisy, H. and Sebag, M. Emergence of Temporal and Spatial Synchronous Behaviors in a Foraging Swarm. In Proc. of European Conference in Artificial Life, ECAL’2011, 125–132, MIT Press. Paris, France. August 2011.
L. Arnold and S. Rebecchi and S. Chevallier and H. PaugamMoisy. An Introduction to Deep Learning [tutorial of special session]. In Proc. of ESANN’2011, Advances in Computational Intelligence and Learning, M. Verleysen Ed., 477–488. Bruges, Belgium, April 2011.
Chevallier, S. and PaugamMoisy, H. and Sebag, M. SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. In Advances in Neural Information Processing Systems 23, NIPS’2010, 379–387. Vancouver, Canada. December 2010.
Mouraud, A. and Guillaume, A. and PaugamMoisy, H. The DAMNED simulator for implementing a dynamic model of the network controlling saccadic eye movements. In Artificial Neural Networks, ICANN’2010, Lecture Notes in Computer Science 6352 :272281, Springer. Thessaloniki, Greece. September 2010.
Arnold, L. PaugamMoisy, H. and Sebag, M. Unsupervised LayerWise Model Selection in Deep Neural Networks. In European Conference on Artificial Intelligence, ECAI’2010, Frontiers in Artificial Intelligence and Applications 215 :915920, IOS Press Amsterdam. Lisbon, Portugal. August 2010.
R. Martinez and H. PaugamMoisy. Algorithms for structural and dynamical polychronous groups detection. In Proc. of ICANN'2009, Lecture Notes in Computer Science 5769, Springer, 7584. Limassol, Cyprus, Sepember 2009.
D. Meunier and H. PaugamMoisy. Neural networks for computational neuroscience [tutorial]. In Proc. of ESANN’2008, Advances in Computational Intelligence and Learning, M. Verleysen Ed., 367–378. Bruges, Belgium, April 2008.
H. PaugamMoisy, R. Martinez and S. Bengio. A supervised learning approach based on STDP and polychronization in spiking neuron networks. In Proc. of ESANN’2007, Advances in Computational Intelligence and Learning, M. Verleysen Ed., 427–432. Bruges, Belgium, April 2007.
A. Mouraud, H. PaugamMoisy and D. Puzenat. DAMNED: A distributed and multithreaded neural event driven simulation framework. In Proc. of Int. Conf. on Parallel Distributed Computing and Networks, PDCN’2006, ACTA Press, 212–217. Innsbruck, Austria, February 2006.
D. Meunier and H. PaugamMoisy. Cluster detection algorithm in neural networks. In Proc. of Europ. Symp. on Artiﬁcial Neural Networks, ESANN’2006, M. Verleysen Ed., 19–24. Bruges, Belgium, April 2006.
S. Chevallier, P. Tarroux and H. PaugamMoisy. Saliency extraction with a distributed spiking neuron network. In Proc. of Europ. Symp. on Artiﬁcial Neural Networks, ESANN’2006, M. Verleysen Ed., 209–214. Bruges, Belgium, April 2006.
A. Mouraud and H. PaugamMoisy. Learning and discrimination through
STDP in a topdown modulated associative memory. In Proc. of
Europ. Symp. on Artiﬁcial Neural Networks, ESANN’2006, M. Verleysen Ed.,
611–616. Bruges, Belgium, April 2006.
D. Meunier and H. PaugamMoisy. Evolutionary supervision of a dynamical neural network allows learning with ongoing weights. In Proc. of Int. Joint Conf. on Neural Networks, IJCNN’2005, IEEEINNS, 1493–1498. Montréal, Canada, July 2005.
E. Reynaud and H. PaugamMoisy. A multiple BAM for heteroassociation and multisensory integration modelling. In Proc. of Int. Joint Conf. on Neural Networks, IJCNN’2005, IEEEINNS, 2117–2122. Montréal, Canada, July 2005.
S. Chevallier, H. PaugamMoisy and F. Lemaître. Distributed processing for modelling realtime multimodal perception in a virtual robot. In Proc. of Int. Conf. on Parallel Distributed Computing and Networks, PDCN’2005, ACTA Press, 393–398. Innsbruck, Austria, February 2005.
D. Meunier and H. PaugamMoisy. A "spiking" Bidirectional Associative Memory for modeling intermodal priming. In Proc. of Int. Conf. on Neural Networks and Computational Intelligence, NCI’2004, ACTA Press. 25–30. Grindelwald, Switzerland, February 2004.
Bouchut, Y., PaugamMoisy, H. and Puzenat, D. Asynchrony in a distributed modular neural network for multimodal integration. In Proc. of Int. Conf. on Parallel and Distributed Computing & Systems, PDCS’2003, ACTA Press, 588593. Marina del Rey, USA, November 2003.
H. PaugamMoisy, D. Puzenat, E. Reynaud and J.Ph. Magué. Neural networks for modelling memory : Case studies [tutorial]. In Proc. of European Conference on Artiﬁcial Neural Networks, ESANN’2002, Dfacto, 7182. Bruges, Belgium, April 2002.
O. Teytaud and H. PaugamMoisy. Bounds on the generalization ability of Bayesian inference and Gibbs algorithms. In Proc. of International Conference on Artiﬁcial Neural Networks, ICANN’2001, Lecture Notes in Computer Science 2130, Springer, 263–268. Vienne, Austria, August 2001.
H. PaugamMoisy and E. Reynaud. Multinetwork system for sensory integration. In Proc. of Int. Joint Conf. on Neural Networks, IJCNN’2001, IEEEINNS, 2343–2348. Washington DC, US, July 2001.
Y. Guermeur, A. Elisseeff and H. PaugamMoisy. A new multiclass SVM based on a uniform convergence result. In Proc. of International Joint Conference on Neural Networks, IJCNN’2000, IEEEINNS, IV :183–188. Como, Italy, July 2000.
H. PaugamMoisy, A. Elisseeff and Y. Guermeur. Generalization performance of multiclass discriminant models. In Proc. of IJCNN’2000, IEEEINNS, IV :177182. Como, Italy, July 2000.
A. Crépet, H. PaugamMoisy, E. Reynaud and D. Puzenat. A modular neural network for binding several modalities. In Proc. of International Conference on Artiﬁcial Intelligence, ICAI’2000, 921–928. CSREA Press. Las Vegas, US, June 2000.
E. Reynaud, A. Crépet, H. PaugamMoisy and D. Puzenat. A modular neural network model for a multimodal associative memory. In Proc. of 4th International Conference on Cognitive and Neural Systems, ICCNS’2000, 17. Boston, US, June 2000.
Y. Guermeur, A. Elisseeff, and H. PaugamMoisy. Estimating the sample complexity of a multiclass discriminant model. In Proc. of 9th International Conference on Artiﬁcial Neural Networks, ICANN’99, 310–315. IEE, 1999.
C. Bertolini, H. PaugamMoisy, and D. Puzenat. Priming an Artiﬁcial Associative Memory. In J. Mira and J.V. SanchezAndres, editors, Proc. of International Workshop on Artiﬁcial Neural Networks, IWANN’99, LNCS, 1606 :348–356. Springer, 1999.
Y. Guermeur, H. PaugamMoisy, and P. Gallinari. Multivariate Linear Regression on Classiﬁer Outputs : A Capacity Study. In L. Niklasson, L. Bodén and T. Ziemke, editors, Proc. of 8th International Conference on Artiﬁcial Neural Networks, ICANN’98, 693–698. Springer, 1998.
A. Elisseeff, and H. PaugamMoisy. Jacobian Neural Network Learning Algorithms. In L. Niklasson, L. Bodén and T. Ziemke, editors, Proc. of 8th International Conference on Artiﬁcial Neural Networks, ICANN’98, 573–578. Springer, 1998.
H. PaugamMoisy. Neural networks : from massively parallel processing towards modular distributed processing. Invited talk, in Proc. of VIII Congresso Latinoamericano de Control Automatica, 1: 31–36. Viña del Mar, CHILI. 1998.
P. Estevez, M. Ugarte, H. PaugamMoisy, and D. Puzenat. Modular Parallel Implementation for HME Neural Networks. In Proc. of 1998 Int. Conf. on Parallel and Distributed Processing Technique and Applications, PDPTA’98, III :1365–1372. CSREA Press, ISBN 1892512068, 1998.
P. Estevez, M. Ugarte, H. PaugamMoisy, and D. Puzenat. Parallel Simulation of Modular Neural Networks. In Proc. of XII Congreso Chileno de Ingenieria Electrica, 555–560. Universidad de La Frontera, Temuco, CHILI, 1997.
G. Brightwell, C. Kenyon, and H. PaugamMoisy. Multilayer neural networks : one or two hidden layers ? In M.C. Mozer, M.I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, Proc. NIPS*96, 148–154. MIT Press, 1997.
A. Elisseeff and H. PaugamMoisy. Size of multilayer networks for exact learning : analytic approach. In M.C. Mozer, M.I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, Proc. NIPS*96, 162–168. MIT Press, 1997.
V. Demian, F. Desprez, H. PaugamMoisy, and M. Pourzandi. Parallel implementation of RBF neural networks. In Parallel Processing, Proc. EuroPar’96, LNCS, 1124 :243–250. Springer, 1996.
B. Girau and H. PaugamMoisy. Load sharing in the training set partition algorithm for parallel neural learning. In 9th Int. Parallel Processing Symposium, Proc. IPPS’95, 586–591. IEEE Press, 1995.
D. Girard and H. PaugamMoisy. Strategies of weight updating for parallel backpropagation. In C. Girault, editor, Applications in Parallel and Distributed Computing, volume A44 of IFIP Transactions, 335–336. NorthHolland, 1994.
A. Azcarraga, H. PaugamMoisy, and D. Puzenat. An incremental neural classiﬁer on a MIMD parallel computer. In C. Girault, editor, Applications in Parallel and Distributed Computing, volume A44 of IFIP Transactions, 13–22. NorthHolland, 1994.
M. Cosnard, P. Koiran, and H. PaugamMoisy. A step towards the
frontier between onehidden layer and twohidden layer neural networks. In
Proc. Int. Joint Conf. Neural Networks, IJCNN’93, 3 :2292–2295. Nagoya,
Japan, 1993.
S. Amghar, H. PaugamMoisy, and J.P. Royet. Learning methods for odor recognition modeling. In B. BouchonMeunier, L. Valverde, and R.R. Yager, editors, Advanced Methods in Artiﬁcial Intelligence, Proc. IPMU’92, LNCS, 682: 361–367. Springer Verlag, 1992.
H. PaugamMoisy. On a parallel algorithm for backpropagation by partitioning the training set. In N. Giambasi, J.C. Rault, and M. Ribes, editors, Proc. NeuroNîmes’92, 53–66. EC2, 1992.
H. PaugamMoisy. Optimal speedup conditions for a parallel backpropagation algorithm. In Parallel Processing, Proc. CONPAR’92VAPP V, L. Bougé, M. Cosnard, Y. Robert, and D. Trystram, editors, LNCS, 634: 719–724. Springer Verlag, 1992.
H. PaugamMoisy. On the convergence of a blockgradient algorithm for backpropagation learning. In Proc. Int. Joint Conf. Neural Networks, IJCNN’92Baltimore, III :919–924, 1992.
M. Cosnard, P. Koiran, and H. PaugamMoisy. Complexity issues in neural network computations. In I. Simon, editor, Latin American Symposium on Theoretical Informatics, Proc. LATIN’92, LNCS, 583: 530–544. Springer Verlag, 1992.
M. Cosnard, J.C. Mignot, and H. PaugamMoisy. Implementations of multilayer neural networks on parallel architectures. In 2nd Int. Specialist Seminar on the Design and Application of Parallel Digital Processors, Conf. Publi. 334: 43–47. IEE, 1991.
H. PaugamMoisy. Selecting and parallelizing neural networks for improving performances. In T. Kohonen, K. M kisara, O. Simula, and J. Kangas, editors, Artiﬁcial Neural Networks, Proc. ICANN91, I :659–664. NorthHolland, 1991.
H. PaugamMoisy. A parallel tool for evaluating learning capabilities of neural networks. In Hamza, M.H., editor, Proc. IASTED Int’al Symposium in : Machine Learning and Neural Networks, 59–62. ACTA Press, 1990.
Last update: May 30th, 2017