Functional Bayesian Neural Networks for Model Uncertainty QuantificationDownload PDF

27 Sept 2018 (modified: 05 May 2023)ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: In this paper, we extend the Bayesian neural network to functional Bayesian neural network with functional Monte Carlo methods that use the samples of functionals instead of samples of networks' parameters for inference to overcome the curse of dimensionality for uncertainty quantification. Based on the previous work on Riemannian Langevin dynamics, we propose the stochastic gradient functional Riemannian dynamics for training functional Bayesian neural network. We show the effectiveness and efficiency of our proposed approach with various experiments.
Data: [MNIST](https://paperswithcode.com/dataset/mnist)
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