Compressive Learning for Semi-Parametric Models. - Michael P. Sheehan; Antoine Gonon; Mike E. Davies 001 (CoRR, 2019)

Approximation speed of quantized vs. unquantized ReLU neural networks and beyond. - Antoine Gonon; Nicolas Brisebarre; Rémi Gribonval; Elisa Riccietti (CoRR, 2022)

Sparsity in neural networks can increase their privacy. - Antoine Gonon; Léon Zheng; Clément Lalanne; Quoc-Tung Le; Guillaume Lauga; Can Pouliquen (CoRR, 2023)

Approximation Speed of Quantized Versus Unquantized ReLU Neural Networks and Beyond. - Antoine Gonon; Nicolas Brisebarre; Rémi Gribonval; Elisa Riccietti (IEEE Trans. Inf. Theory, 2023)

A path-norm toolkit for modern networks: consequences, promises and challenges. - Antoine Gonon; Nicolas Brisebarre; Elisa Riccietti; Rémi Gribonval (ICLR, 2024)

Path-metrics, pruning, and generalization. - Antoine Gonon; Nicolas Brisebarre; Elisa Riccietti; Rémi Gribonval (CoRR, 2024)

Make Inference Faster: Efficient GPU Memory Management for Butterfly Sparse Matrix Multiplication. - Antoine Gonon; Léon Zheng; Pascal Carrivain; Quoc-Tung Le (CoRR, 2024)

Harnessing symmetries for modern deep learning challenges: a path-lifting perspective. (Exploiter les symétries pour relever les défis modernes de l'apprentissage profond: une perspective par le relèvement dans l'espace des chemins). - Antoine Gonon (2024)