45 citations · 100 across the 29 of their papers we have counts for
10 papers · 1 filter
Training Modern Deep Neural Networks for Memory-Fault Robustness
Ghouthi Boukli Hacene, François Leduc-Primeau, Amal Ben Soussia +2
Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging. In this paper, we inve…
Efficient Hardware Implementation of Incremental Learning and Inference on Chip
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation o…
Deep geometric knowledge distillation with graphs
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene +3
In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resour…
Improved Visual Localization via Graph Smoothing
Carlos Lassance, Yasir Latif, Ravi Garg +2
Vision based localization is the problem of inferring the pose of the camera given a single image. One solution to this problem is to learn a deep neural network to infer the pose…
Structural Robustness for Deep Learning Architectures
Carlos Lassance, Vincent Gripon, Jian Tang +1
Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including…
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Myriam Bontonou, Carlos Lassance, Vincent Gripon +1
Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging. Yet it is a highly difficult pro…