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of 3
pro vyhledávání: '"Ribeiro, Lucas Grativol"'
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