17 citations · 24 across the 7 of their papers we have counts for
7 papers
SLAD : Shared LoRA Adapters for Task Specific Distillation
Reda Bensaid, Yassir Bendou, Vincent Gripon +1
In the context of resource-constrained environments such as embedded systems, adapting reduced-size foundation models to downstream tasks has become increasingly popular. This has…
MUKA: Multi Kernel Audio Adaptation Of Audio-Language Models
Reda Bensaid, Amine Ouasfi, Yassir Bendou +4
Multimodal foundation models have demonstrated impressive generalization capabilities, yet efficiently adapting them to new tasks in a few-shot setting remains a critical challenge…
A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding
Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon +1
Massive multiple-input multiple-output (mMIMO) downlink precoding offers high spectral efficiency but remains challenging to deploy in practice because near-optimal algorithms such…
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
Ali Hasanzadeh Karkan, Hamed Hojatian, Jean-François Frigon +1
Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems. Nevertheless, it remains challeng…
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
Hamed Hojatian, Zoubeir Mlika, Jérémy Nadal +2
Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency~(EE) of massive multiple-input multiple-output~(mMIMO) systems. However,…
SAMSON: Sharpness-Aware Minimization Scaled by Outlier Normalization for Improving DNN Generalization and Robustness
Gonçalo Mordido, Sébastien Henwood, Sarath Chandar +1
Energy-efficient deep neural network (DNN) accelerators are prone to non-idealities that degrade DNN performance at inference time. To mitigate such degradation, existing methods t…