Begon, J.-M. (2021). Supervised machine learning under constraints [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/264493
As supervised learning occupies a larger and larger place in our everyday life, it is met with mo...
Begon, J.-M., & Geurts, P. (2021). Sample-Free White-Box Out-of-Distribution Detection for Deep Learning. IEEE Conference on Computer Vision and Pattern Recognition. Proceedings. doi:10.1109/CVPRW53098.2021.00367
Being able to detect irrelevant test examples with respect to deployed deep learning models is pa...
Peer reviewed
Vecoven, N., Begon, J.-M., Sutera, A., Geurts, P., & Huynh-Thu, V. A. (2020). Nets versus trees for feature ranking and gene network inference. In Proceeding of the 23rd International Conference on Discovery Science (DS 2020). Springer. doi:10.1007/978-3-030-61527-7_16
We investigate several global variable importance measures derived from artificial neural network...
Peer reviewed
Vecoven, N., Begon, J.-M., Huynh-Thu, V. A., & Geurts, P. (2017). Nets versus trees for feature ranking and gene network inference. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/231719.
We propose to tackle the challenging problem of gene regulatory network inference, using variable...
Begon, J.-M. (2017). A random walk in Machine Learning [Paper presentation]. Geeks anonymes, Liège, Belgium.
Since the dawn of machine learning (ML), it hasn’t stop spreading into our everyday lives in new,...
Begon, J.-M., Joly, A., & Geurts, P. (2017). Globally Induced Forest: A Prepruning Compression Scheme. Proceedings of Machine Learning Research, 70, 420-428.
Tree-based ensemble models are heavy memory- wise. An undesired state of affairs consider- ing no...
Peer Reviewed verified by ORBi
Begon, J.-M., Joly, A., & Geurts, P. (12 September 2016). Joint learning and pruning of decision forests [Paper presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium.
Decision forests such as Random Forests and Extremely randomized trees are state-of-the-art super...
Mormont, R., Begon, J.-M., Hoyoux, R., & Marée, R. (12 September 2016). SLDC: an open-source workflow for object detection in multi-gigapixel images [Paper presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium.
Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7. doi:10.1093/bioinformatics/btw013
Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific
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Peer Reviewed verified by ORBi
Begon, J.-M. (2014). Generic image classification: random and convolutional approaches [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/191570
Supervised learning introduces genericity in the field of image classification, thus enabling fas...
Begon, J.-M. (2011). Prototypage d'un serveur de données géographiques maillées: Rasdaman [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/197483
In this thesis, we assessed the capability of the Rasdaman software as a full-scale raster data s...
