The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Joshua Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9289-9299, 2020.

Abstract

Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance. In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization. For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence. This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models’ performance. Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-swiatkowski20a, title = {The k-tied Normal Distribution: A Compact Parameterization of {G}aussian Mean Field Posteriors in {B}ayesian Neural Networks}, author = {Swiatkowski, Jakub and Roth, Kevin and Veeling, Bastiaan and Tran, Linh and Dillon, Joshua and Snoek, Jasper and Mandt, Stephan and Salimans, Tim and Jenatton, Rodolphe and Nowozin, Sebastian}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {9289--9299}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/swiatkowski20a/swiatkowski20a.pdf}, url = { http://proceedings.mlr.press/v119/swiatkowski20a.html }, abstract = {Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance. In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization. For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence. This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models’ performance. Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.} }
Endnote
%0 Conference Paper %T The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks %A Jakub Swiatkowski %A Kevin Roth %A Bastiaan Veeling %A Linh Tran %A Joshua Dillon %A Jasper Snoek %A Stephan Mandt %A Tim Salimans %A Rodolphe Jenatton %A Sebastian Nowozin %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-swiatkowski20a %I PMLR %P 9289--9299 %U http://proceedings.mlr.press/v119/swiatkowski20a.html %V 119 %X Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance. In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization. For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence. This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models’ performance. Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.
APA
Swiatkowski, J., Roth, K., Veeling, B., Tran, L., Dillon, J., Snoek, J., Mandt, S., Salimans, T., Jenatton, R. & Nowozin, S.. (2020). The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:9289-9299 Available from http://proceedings.mlr.press/v119/swiatkowski20a.html .

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