3 papers
cs.CV2026
Efficient Few-Shot Learning for Edge AI via Knowledge Distillation on MobileViT
Shuhei Tsuyuki, Reda Bensaid, Jérémy Morlier +4
Efficient and adaptable deep learning models are an important area of deep learning research, driven by the need for highly efficient models on edge devices. Few-shot learning enab…
cs.LG2026
MONET: Modeling and Optimization of neural NEtwork Training from Edge to Data Centers
Jérémy Morlier, Robin Geens, Stef Cuyckens +4
While hardware-software co-design has significantly improved the efficiency of neural network inference, modeling the training phase remains a critical yet underexplored challenge.…
cs.LG2025
Input Resolution Downsizing as a Compression Technique for Vision Deep Learning Systems
Jeremy Morlier, Mathieu Leonardon, Vincent Gripon
Model compression is a critical area of research in deep learning, in particular in vision, driven by the need to lighten models memory or computational footprints. While numerous…