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Dependence between Bayesian neural network units
Mariia Vladimirova · Julyan Arbel · Stephane Girard
Event URL: https://openreview.net/forum?id=B9DcC7nSPsj »
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.
Author Information
Mariia Vladimirova (Inria)
Julyan Arbel
Stephane Girard
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