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20222026
most citedLaw of large numbers and central limit theorem for wide two-layer neural networks: the mini-batch and noisy case

2 citations · 3 across the 9 of their papers we have counts for

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cs.NE2026

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…

cs.NE2026

Uniform-in-time concentration in two-layer neural networks via transportation inequalities

Arnaud Guillin, Boris Nectoux, Paul Stos

We quantify, uniformly over time and with high probability, the discrepancy between the predictions of a two-layer neural network trained by stochastic gradient descent (SGD) and t…