An Overview of Uncertainty Quantification Methods for Infinite Neural Networks

• URL: http://arxiv.org/abs/2201.04746v1
• Date: Thu, 13 Jan 2022 00:03:22 GMT
• Title: An Overview of Uncertainty Quantification Methods for Infinite Neural Networks
• Authors: Florian Juengermann, Maxime Laasri, Marius Merkle
• Abstract summary: We review methods for quantifying uncertainty in infinite-width neural networks. We make use of several equivalence results along the way to obtain exact closed-form solutions for predictive uncertainty.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To better understand the theoretical behavior of large neural networks, several works have analyzed the case where a network's width tends to infinity. In this regime, the effect of random initialization and the process of training a neural network can be formally expressed with analytical tools like Gaussian processes and neural tangent kernels. In this paper, we review methods for quantifying uncertainty in such infinite-width neural networks and compare their relationship to Gaussian processes in the Bayesian inference framework. We make use of several equivalence results along the way to obtain exact closed-form solutions for predictive uncertainty.

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