NeurIPS 2019poster153 citations
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
Abstract
We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It extends neural network libraries with drop-in replacements for common layers. This enables composition via a unified abstraction over deterministic and stochastic functions and allows for scalability via the underlying system. These layers capture uncertainty over weights (Bayesian neural nets), pre-activation units (dropout), activations (
BibTeX
@inproceedings{NEURIPS2019_154ff894,
author = {Tran, Dustin and Dusenberry, Mike and van der Wilk, Mark and Hafner, Danijar},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Bayesian Layers: A Module for Neural Network Uncertainty},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/154ff8944e6eac05d0675c95b5b8889d-Paper.pdf},
volume = {32},
year = {2019}
}