Dependence between Bayesian neural network units
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 29 Nov 2021.
Builds on
10 of 15 references.
References · 10
- 2017Deep Neural Networks as Gaussian Processescited by 617
- 2018Gaussian Process Behaviour in Wide Deep Neural Networkscited by 314
- 2018Deep Convolutional Networks as shallow Gaussian Processescited by 137
- 2021Bayesian Neural Network Priors Revisitedcited by 121
- 2019Why bigger is not always better: on finite and infinite neural networkscited by 41
- 2021Precise characterization of the prior predictive distribution of deep ReLU networkscited by 28
- 2020Stable behaviour of infinitely wide deep neural networkscited by 23
- 2021The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspectivecited by 21
- 2021Bayesian neural network unit priors and generalized Weibull-tail propertycited by 5
- 2021Correlated Weights in Infinite Limits of Deep Convolutional Neural Networkscited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.