Modulated Self-attention Convolutional Network for VQA. - Jean-Benoit Delbrouck; Antoine Maiorca; Nathan Hubens; Stéphane Dupont (CoRR, 2019)

An Experimental Study of the Impact of Pre-Training on the Pruning of a Convolutional Neural Network. - Nathan Hubens; Matei Mancas; Marc Decombas; Marius Preda; Titus Zaharia; Bernard Gosselin; Thierry Dutoit (APPIS, 2020)

One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget. - Nathan Hubens; Matei Mancas; Bernard Gosselin; Marius Preda; Titus Zaharia (CoRR, 2021)

An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network. - Nathan Hubens; Matei Mancas; Bernard Gosselin; Marius Preda; Titus Zaharia (CoRR, 2021)

Towards Lightweight Neural Animation: Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models. - Antoine Maiorca; Nathan Hubens; Sohaib Laraba; Thierry Dutoit (VISIGRAPP (1: GRAPP), 2022)

Improve Convolutional Neural Network Pruning by Maximizing Filter Variety. - Nathan Hubens; Matei Mancas; Bernard Gosselin; Marius Preda; Titus Zaharia (ICIAP (1), 2022)

One-Cycle Pruning: Pruning Convnets With Tight Training Budget. - Nathan Hubens; Matei Mancas; Bernard Gosselin; Marius Preda; Titus Zaharia (ICIP, 2022)

Where Is My Mind (looking at)? Predicting Visual Attention from Brain Activity. - Victor Delvigne; Noé Tits; Luca La Fisca; Nathan Hubens; Antoine Maiorca; Hazem Wannous; Thierry Dutoit; Jean-Philippe Vandeborre (CoRR, 2022)

FasterAI: A Lightweight Library for Creating Sparse Neural Networks. - Nathan Hubens (CoRR, 2022)

Where Is My Mind (Looking at)? A Study of the EEG-Visual Attention Relationship. - Victor Delvigne; Noé Tits; Luca La Fisca; Nathan Hubens; Antoine Maiorca; Hazem Wannous; Thierry Dutoit; Jean-Philippe Vandeborre (Informatics, 2022)

Towards lighter and faster deep neural networks with parameter pruning. (Compression et accélération de réseaux de neurones profonds par élagage synaptique). - Nathan Hubens (2022)

Induced Feature Selection by Structured Pruning. - Nathan Hubens; Victor Delvigne; Matei Mancas; Bernard Gosselin; Marius Preda; Titus Zaharia (CoRR, 2023)

A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation. - Omar Seddati; Nathan Hubens; Stéphane Dupont; Thierry Dutoit (CoRR, 2023)