AISTATS 2022poster29 citations
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
Abstract
Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provided theoretical insight into their priors and posteriors. However, we have no analogous insight into their posteriors under approximate inference. In this work, we show that mean-field variational inference
BibTeX
@InProceedings{pmlr-v151-coker22a,
title = { Wide Mean-Field Bayesian Neural Networks Ignore the Data },
author = {Coker, Beau and Bruinsma, Wessel P. and Burt, David R. and Pan, Weiwei and Doshi-Velez, Finale},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {5276--5333},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/coker22a/coker22a.pdf},
url = {https://proceedings.mlr.press/v151/coker22a.html},
abstract = { Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provided theoretical insight into their priors and posteriors. However, we have no analogous insight into their posteriors under approximate inference. In this work, we show that mean-field variational inference