Universal approximations of permutation invariant/equivariant functions by deep neural networks. - Akiyoshi Sannai; Yuuki Takai; Matthieu Cordonnier (CoRR, 2019)

On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity. - Yuuki Takai; Akiyoshi Sannai; Matthieu Cordonnier (AISTATS, 2021)

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. - Matthieu Cordonnier; Nicolas Keriven; Nicolas Tremblay; Samuel Vaiter (CoRR, 2023)

Seeking Universal Approximation for Continuous Limits of Graph Neural Networks on Large Random Graphs. - Matthieu Cordonnier; Nicolas Keriven; Nicolas Tremblay; Samuel Vaiter (IEEECONF, 2024)

Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry. - Akiyoshi Sannai; Yuuki Takai; Matthieu Cordonnier (Trans. Mach. Learn. Res., 2024)