Armand Foucault — accepted AI-conference papers

2025

HADAMRNN: BINARY AND SPARSE TERNARY ORTHOGONAL RNNS

Armand Foucault, Francois Malgouyres, Franck Mamalet

ICLR 2025poster

Binary and sparse ternary weights in neural networks enable faster computations and lighter representations, facilitating their use on edge devices with limited computational power. Meanwhile, vanilla RNNs are highly sensitive to changes in their recurrent weights, making the binarization and ternar…

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2022

A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks

El Mehdi Achour, Armand Foucault, Sébastien Gerchinovitz, Francois Malgouyres

NeurIPS 2022accept

We study the fundamental limits to the expressive power of neural networks. Given two sets $F$, $G$ of real-valued functions, we first prove a general lower bound on how well functions in $F$ can be approximated in $L^p(\mu)$ norm by functions in $G$, for any $p \geq 1$ and any probability measure $…

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