Sébastien Adam
Sébastien Adam
Professor, LITIS, University of Rouen
Verifisert e-postadresse på univ-rouen.fr - Startside
Sitert av
Sitert av
Analyzing the expressive power of graph neural networks in a spectral perspective
M Balcilar, G Renton, P Héroux, B Gaüzère, S Adam, P Honeine
International Conference on Learning Representations, 2021
Influence of hyperparameters on random forest accuracy
S Bernard, L Heutte, S Adam
Multiple Classifier Systems: 8th International Workshop, MCS 2009, Reykjavik …, 2009
Dynamic random forests
S Bernard, S Adam, L Heutte
Pattern Recognition Letters 33 (12), 1580-1586, 2012
Learning to detect tables in scanned document images using line information
T Kasar, P Barlas, S Adam, C Chatelain, T Paquet
2013 12th International Conference on Document Analysis and Recognition …, 2013
Handwritten text line segmentation using fully convolutional network
G Renton, C Chatelain, S Adam, C Kermorvant, T Paquet
2017 14th IAPR International Conference on Document Analysis and Recognition …, 2017
On the selection of decision trees in random forests
S Bernard, L Heutte, S Adam
2009 International joint conference on neural networks, 302-307, 2009
Using random forests for handwritten digit recognition
S Bernard, S Adam, L Heutte
Ninth international conference on document analysis and recognition (ICDAR …, 2007
Breaking the limits of message passing graph neural networks
M Balcilar, P Héroux, B Gauzere, P Vasseur, S Adam, P Honeine
International Conference on Machine Learning, 599-608, 2021
Symbol and character recognition: application to engineering drawings
S Adam, JM Ogier, C Cariou, R Mullot, J Labiche, J Gardes
International Journal on Document Analysis and Recognition 3, 89-101, 2000
New binary linear programming formulation to compute the graph edit distance
J Lerouge, Z Abu-Aisheh, R Raveaux, P Héroux, S Adam
Pattern Recognition 72, 254-265, 2017
Forest-RK: A new random forest induction method
S Bernard, L Heutte, S Adam
Advanced Intelligent Computing Theories and Applications. With Aspects of …, 2008
Spotting L3 slice in CT scans using deep convolutional network and transfer learning
S Belharbi, C Chatelain, R Hérault, S Adam, S Thureau, M Chastan, ...
Computers in biology and medicine 87, 95-103, 2017
A multi-model selection framework for unknown and/or evolutive misclassification cost problems
C Chatelain, S Adam, Y Lecourtier, L Heutte, T Paquet
Pattern Recognition 43 (3), 815-823, 2010
An integer linear program for substitution-tolerant subgraph isomorphism and its use for symbol spotting in technical drawings
P Le Bodic, P Héroux, S Adam, Y Lecourtier
Pattern Recognition 45 (12), 4214-4224, 2012
Graph edit distance contest: Results and future challenges
Z Abu-Aisheh, B Gaüzere, S Bougleux, JY Ramel, L Brun, R Raveaux, ...
Pattern Recognition Letters 100, 96-103, 2017
The multiclass ROC front method for cost-sensitive classification
S Bernard, C Chatelain, S Adam, R Sabourin
Pattern Recognition 52, 46-60, 2016
A general framework for the evaluation of symbol recognition methods
E Valveny, P Dosch, A Winstanley, Y Zhou, S Yang, L Yan, L Wenyin, ...
International Journal of Document Analysis and Recognition (IJDAR) 9, 59-74, 2007
A typed and handwritten text block segmentation system for heterogeneous and complex documents
P Barlas, S Adam, C Chatelain, T Paquet
2014 11th IAPR International Workshop on Document Analysis Systems, 46-50, 2014
Symbol spotting using full visibility graph representation
H Locteau, S Adam, E Trupin, J Labiche, P Héroux
Workshop on Graphics Recognition, 49-50, 2007
Predicting experimental electrophilicities from quantum and topological descriptors: a machine learning approach
G Hoffmann, M Balcilar, V Tognetti, P Héroux, B Gaüzère, S Adam, ...
Journal of Computational Chemistry 41 (24), 2124-2136, 2020
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