3 citations · 6 across the 13 of their papers we have counts for
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REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
Yassine El Ouahidi, Jonathan Lys, Philipp Thölke +5
Foundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has…
TensLoRA: Tensor Alternatives for Low-Rank Adaptation
Axel Marmoret, Reda Bensaid, Jonathan Lys +2
Low-Rank Adaptation (LoRA) is widely used to efficiently adapt Transformers by adding trainable low-rank matrices to attention projections. While effective, these matrices are cons…
ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models
Yassir Bendou, Amine Ouasfi, Vincent Gripon +1
The growing popularity of Contrastive Language-Image Pretraining (CLIP) has led to its widespread application in various visual downstream tasks. To enhance CLIP's effectiveness an…