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 | 258* | 2021 |
Influence of hyperparameters on random forest accuracy S Bernard, L Heutte, S Adam Multiple Classifier Systems: 8th International Workshop, MCS 2009, Reykjavik …, 2009 | 228 | 2009 |
Dynamic random forests S Bernard, S Adam, L Heutte Pattern Recognition Letters 33 (12), 1580-1586, 2012 | 164 | 2012 |
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 | 156* | 2017 |
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 | 156 | 2013 |
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 | 156 | 2009 |
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 | 136 | 2021 |
Using random forests for handwritten digit recognition S Bernard, S Adam, L Heutte Ninth international conference on document analysis and recognition (ICDAR …, 2007 | 135 | 2007 |
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 | 127 | 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 | 73 | 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 | 70 | 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 | 66 | 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 | 49 | 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 | 46 | 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 | 45 | 2017 |
The multiclass ROC front method for cost-sensitive classification S Bernard, C Chatelain, S Adam, R Sabourin Pattern Recognition 52, 46-60, 2016 | 40 | 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 | 38 | 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 | 36 | 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 | 36 | 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 | 35 | 2020 |