most citedVariational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

stat.ML2024

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference

Arnaud Descours, Tom Huix, Arnaud Guillin +3

In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regress…

stat.ML2024

Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians

Tom Huix, Anna Korba, Alain Durmus +1

Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a…

stat.ML2022★ 2 cited

Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study

Tom Huix, Szymon Majewski, Alain Durmus +2

This paper studies the Variational Inference (VI) used for training Bayesian Neural Networks (BNN) in the overparameterized regime, i.e., when the number of neurons tends to infini…