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cs.LG2026
Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training
Jonathan Lys, Vincent Gripon, Bastien Pasdeloup +4
Deep Learning architectures, and in particular Transformers, are conventionally viewed as a composition of layers. These layers are actually often obtained as the sum of two contri…
cs.LG2025
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…
cs.LG2025
Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning
Manon Renault, Hamoud Younes, Hugo Tessier +3
Package monitoring is an important topic in industrial applications, with significant implications for operational efficiency and ecological sustainability. In this study, we propo…