Search Results for author: Ghouthi Boukli Hacene

Found 13 papers, 3 papers with code

DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems

no code implementations2 Dec 2020 Vincent Gripon, Carlos Lassance, Ghouthi Boukli Hacene

Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years.

ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget

no code implementations20 Jul 2020 Guillaume Coiffier, Ghouthi Boukli Hacene, Vincent Gripon

Typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations.

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization

no code implementations8 Feb 2020 Miloš Nikolić, Ghouthi Boukli Hacene, Ciaran Bannon, Alberto Delmas Lascorz, Matthieu Courbariaux, Yoshua Bengio, Vincent Gripon, Andreas Moshovos

Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time and energy benefits on existing hardware designs that support short bitlengths.


Training Modern Deep Neural Networks for Memory-Fault Robustness

no code implementations23 Nov 2019 Ghouthi Boukli Hacene, François Leduc-Primeau, Amal Ben Soussia, Vincent Gripon, François Gagnon

Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging.

Deep geometric knowledge distillation with graphs

1 code implementation8 Nov 2019 Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega

Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces.

Knowledge Distillation

Attention Based Pruning for Shift Networks

1 code implementation29 May 2019 Ghouthi Boukli Hacene, Carlos Lassance, Vincent Gripon, Matthieu Courbariaux, Yoshua Bengio

In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods.

Object Recognition

Transfer Incremental Learning using Data Augmentation

no code implementations4 Oct 2018 Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jezequel

Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power.

Data Augmentation Incremental Learning

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