Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Bayesian Deep Learning

Dependence between Bayesian neural network units

Mariia Vladimirova · Julyan Arbel · Stephane Girard


Abstract:

The connection between Bayesian neural networks and Gaussian processes gained a~lot of attention in the last few years, with the flagship result that hidden units converge to a Gaussian process limit when the layers width tends to infinity. Underpinning this result is the fact that hidden units become independent in the infinite-width limit. Our aim is to shed some light on hidden units dependence properties in practical finite-width Bayesian neural networks. In addition to theoretical results, we assess empirically the depth and width impacts on hidden units dependence properties.

Chat is not available.