Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Arzel, Matthieu"'
Autor:
Ribeiro, Lucas Grativol, Leonardon, Mathieu, Muller, Guillaume, Fresse, Virginie, Arzel, Matthieu
Publikováno v:
32nd European Signal Processing Conference EUSIPCO, Aug 2024, Lyon, France
Low-Rank Adaptation (LoRA) methods have gained popularity in efficient parameter fine-tuning of models containing hundreds of billions of parameters. In this work, instead, we demonstrate the application of LoRA methods to train small-vision models i
Externí odkaz:
http://arxiv.org/abs/2406.14082
Autor:
Ribeiro, Lucas Grativol, Gauthier, Lubin, Leonardon, Mathieu, Morlier, Jérémy, Lavrard-Meyer, Antoine, Muller, Guillaume, Fresse, Virginie, Arzel, Matthieu
Publikováno v:
ISCAS 2024 : IEEE International Symposium on Circuits and Systems, May 2024, Singapore, Singapore
This paper tackles the challenges of implementing few-shot learning on embedded systems, specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks, especially when the costs of data acquisition or labeling prove to be proh
Externí odkaz:
http://arxiv.org/abs/2404.19354
Autor:
Ribeiro, Lucas Grativol, Leonardon, Mathieu, Muller, Guillaume, Fresse, Virginie, Arzel, Matthieu
Publikováno v:
IEEE 30th International Conference on Electronics, Circuits and Systems, Dec 2023, Istanbul, Turkey
Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacysensitive applications such as those in military and medical domains, where client data cannot be shared or transferred to a cloud comp
Externí odkaz:
http://arxiv.org/abs/2310.14693
Autor:
Tessier, Hugo, Gripon, Vincent, Léonardon, Mathieu, Arzel, Matthieu, Bertrand, David, Hannagan, Thomas
Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitations in terms of energy consumption prohibit the use of very large netwo
Externí odkaz:
http://arxiv.org/abs/2206.06255
Autor:
Tessier, Hugo, Gripon, Vincent, Léonardon, Mathieu, Arzel, Matthieu, Bertrand, David, Hannagan, Thomas
Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many computer vision tasks. However, depending on the architecture, pruning introduces dimensional discrepancies which preven
Externí odkaz:
http://arxiv.org/abs/2206.06247
Autor:
Tessier, Hugo, Gripon, Vincent, Léonardon, Mathieu, Arzel, Matthieu, Hannagan, Thomas, Bertrand, David
Publikováno v:
Journal of Imaging 8 (2022), no. 3: 64
Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks. Despite many innovations in recent decades, pruning approaches still face core issues that hinder their performance or sca
Externí odkaz:
http://arxiv.org/abs/2011.10520
Autor:
Hacene, Ghouthi Boukli, Gripon, Vincent, Farrugia, Nicolas, Arzel, Matthieu, Jezequel, Michel
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves s
Externí odkaz:
http://arxiv.org/abs/1911.07847
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits
Externí odkaz:
http://arxiv.org/abs/1812.11337
Autor:
Hacene, Ghouthi Boukli, Gripon, Vincent, Farrugia, Nicolas, Arzel, Matthieu, Jezequel, Michel
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which consi
Externí odkaz:
http://arxiv.org/abs/1810.02020
Autor:
Lecureur, Valérie, Arzel, Matthieu, Ameziane, Sarah, Houlbert, Noémie, Le Vee, Marc, Jouneau, Stéphane, Fardel, Olivier
Publikováno v:
In Toxicology 2012 292(1):13-22