We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

Functional Bayesian Neural Networks for Model Uncertainty Quantification

lib:91cc486e96cfc69b (v1.0.0)

Authors: Nanyang Ye,Zhanxing Zhu
Where published: ICLR 2019 5
Document:  PDF  DOI 
Abstract URL: https://openreview.net/forum?id=SJxFN3RcFX

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.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!