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- Pierre Geurts
Université de Liège, Belgium
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Publication search results
found 108 matches
- 2024
- Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, Henrik Boström:
Can local explanation techniques explain linear additive models? Data Min. Knowl. Discov. 38(1): 237-280 (2024) - Vân Anh Huynh-Thu, Pierre Geurts:
Optimizing model-agnostic random subspace ensembles. Mach. Learn. 113(2): 993-1042 (2024) - 2023
- Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts:
Knowledge-Guided Additive Modeling for Supervised Regression. DS 2023: 64-78 - Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts:
Knowledge-Guided Additive Modeling For Supervised Regression. CoRR abs/2307.02229 (2023) - 2022
- Navdeep Kumar, Claudia Di Biagio, Zachary Dellacqua, Ratish Raman, Arianna Martini, Clara Boglione, Marc Muller, Pierre Geurts, Raphaël Marée:
Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages. ECCV Workshops (4) 2022: 470-486 - Romain Mormont, Mehdi Testouri, Raphaël Marée, Pierre Geurts:
Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-training. ECCV Workshops (7) 2022: 577-592 - Jean-Michel Begon, Pierre Geurts:
Distillation from heterogeneous unlabeled collections. CoRR abs/2201.06507 (2022) - 2021
- Matthia Sabatelli, Nikolay Banar, Marie Cocriamont, Eva Coudyzer, Karine Lasaracina, Walter Daelemans, Pierre Geurts, Mike Kestemont:
Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. Digit. Humanit. Q. 15(1) (2021) - Romain Mormont, Pierre Geurts, Raphaël Marée:
Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology. IEEE J. Biomed. Health Informatics 25(2): 412-421 (2021) - Navdeep Kumar, Alessio Carletti, Paulo J. Gavaia, Marc Muller, M. Leonor Cancela, Pierre Geurts, Raphaël Marée:
Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images. CAIP (1) 2021: 154-164 - Jean-Michel Begon, Pierre Geurts:
Sample-Free White-Box Out-of-Distribution Detection for Deep Learning. CVPR Workshops 2021: 3290-3299 - Nikolay Banar, Matthia Sabatelli, Pierre Geurts, Walter Daelemans, Mike Kestemont:
Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain. Computer Vision and Image Analysis of Art 2021: 1-9 - Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
From global to local MDI variable importances for random forests and when they are Shapley values. NeurIPS 2021: 3533-3543 - Matthia Sabatelli, Mike Kestemont, Pierre Geurts:
On the Transferability of Winning Tickets in Non-natural Image Datasets. VISIGRAPP (5: VISAPP) 2021: 59-69 - Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, Henrik Boström:
Evaluation of Local Model-Agnostic Explanations Using Ground Truth. CoRR abs/2106.02488 (2021) - Vân Anh Huynh-Thu, Pierre Geurts:
Optimizing model-agnostic Random Subspace ensembles. CoRR abs/2109.03099 (2021) - Matthia Sabatelli, Pierre Geurts:
On The Transferability of Deep-Q Networks. CoRR abs/2110.02639 (2021) - Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
From global to local MDI variable importances for random forests and when they are Shapley values. CoRR abs/2111.02218 (2021) - 2020
- Ivica Slavkov, Matej Petkovic, Pierre Geurts, Dragi Kocev, Saso Dzeroski:
Error curves for evaluating the quality of feature rankings. PeerJ Comput. Sci. 6: e310 (2020) - Nicolas Vecoven, Jean-Michel Begon, Antonio Sutera, Pierre Geurts, Vân Anh Huynh-Thu:
Nets Versus Trees for Feature Ranking and Gene Network Inference. DS 2020: 231-245 - Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN 2020: 1-8 - Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe:
Artificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers. Communications in Computer and Information Science 1196, Springer 2020, ISBN 978-3-030-65153-4 [contents] - Romain Mormont, Pierre Geurts, Raphaël Marée:
Multi-task pre-training of deep neural networks for digital pathology. CoRR abs/2005.02561 (2020) - Matthia Sabatelli, Mike Kestemont, Pierre Geurts:
On the Transferability of Winning Tickets in Non-Natural Image Datasets. CoRR abs/2005.05232 (2020) - Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli:
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2012.12062 (2020) - 2019
- Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. BNAIC/BENELEARN 2019 - Katrien Beuls, Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe, Paul Van Eecke:
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019. CEUR Workshop Proceedings 2491, CEUR-WS.org 2019 [contents] - Arnaud Joly, Louis Wehenkel, Pierre Geurts:
Gradient tree boosting with random output projections for multi-label classification and multi-output regression. CoRR abs/1905.07558 (2019) - Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. CoRR abs/1909.01779 (2019) - 2018
- Konstantinos Pliakos, Pierre Geurts, Celine Vens:
Global multi-output decision trees for interaction prediction. Mach. Learn. 107(8-10): 1257-1281 (2018)
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