2 citations · 2 across the 5 of their papers we have counts for
6 papers
Faster PMNS Multi-precision Multiplications Using Truncated Montgomery Technique
Laurent-Stéphane Didier, Alexy Dutois-Ruiz, Jean-Marc Robert
The Polynomial Modular Number Systems (PMNS) aim to represent elements of fields or rings of large characteristics using polynomials satisfying bounds on some parameters (degree, a…
Quantifying Uncertainty In Wide Two-Layer Neural Networks: On The Law Of The Limiting Fluctuation Process
Arnaud Descours, Arnaud Guillin, Geoffrey Lacour +3
Uncertainty quantification in neural networks prediction is a main issue for usual applications. Our approach seeks at reducing computation costs by directly evaluating uncertainty…
Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning
Arnaud Descours, Léonard Deroose, Jan Ramon
Federated Learning (FL) enables decentralized model training across multiple clients while optionally preserving data privacy. However, communication efficiency remains a critical…
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…
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference
Arnaud Descours, Tom Huix, Arnaud Guillin +3
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case. We consider a regression problem with…
Law of large numbers and central limit theorem for wide two-layer neural networks: the mini-batch and noisy case
Arnaud Descours, Arnaud Guillin, Manon Michel +1
In this work, we consider a wide two-layer neural network and study the behavior of its empirical weights under a dynamics set by a stochastic gradient descent along the quadratic…