28 citations · 31 across the 6 of their papers we have counts for
6 papers
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…
ThinResNet: A New Baseline for Structured Convolutional Networks Pruning
Hugo Tessier, Ghouti Boukli Hacene, Vincent Gripon
Pruning is a compression method which aims to improve the efficiency of neural networks by reducing their number of parameters while maintaining a good performance, thus enhancing…
Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU
Hugo Tessier, Vincent Gripon, Mathieu Léonardon +3
Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitatio…
Leveraging Structured Pruning of Convolutional Neural Networks
Hugo Tessier, Vincent Gripon, Mathieu Léonardon +3
Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many computer vision tasks. However, depending on the a…
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
Yassine El Ouahidi, Hugo Tessier, Giulia Lioi +3
Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the b…
Rethinking Weight Decay For Efficient Neural Network Pruning
Hugo Tessier, Vincent Gripon, Mathieu Léonardon +3
Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks. Despite many innovations in recent decades, pruning…