Winsorization for Robust Bayesian Neural Networks
With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. W...
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MDPI AG
2021
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oai:doaj.org-article:911e03cac136460fa0bfa8c32c4cbcc32021-11-25T17:30:52ZWinsorization for Robust Bayesian Neural Networks10.3390/e231115461099-4300https://doaj.org/article/911e03cac136460fa0bfa8c32c4cbcc32021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1546https://doaj.org/toc/1099-4300With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.Somya SharmaSnigdhansu ChatterjeeMDPI AGarticleBayesian neural networkuncertainty quantificationvariational Gaussian processWinsorizationconcrete dropoutflipoutScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1546, p 1546 (2021) |
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Bayesian neural network uncertainty quantification variational Gaussian process Winsorization concrete dropout flipout Science Q Astrophysics QB460-466 Physics QC1-999 |
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Bayesian neural network uncertainty quantification variational Gaussian process Winsorization concrete dropout flipout Science Q Astrophysics QB460-466 Physics QC1-999 Somya Sharma Snigdhansu Chatterjee Winsorization for Robust Bayesian Neural Networks |
description |
With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust. |
format |
article |
author |
Somya Sharma Snigdhansu Chatterjee |
author_facet |
Somya Sharma Snigdhansu Chatterjee |
author_sort |
Somya Sharma |
title |
Winsorization for Robust Bayesian Neural Networks |
title_short |
Winsorization for Robust Bayesian Neural Networks |
title_full |
Winsorization for Robust Bayesian Neural Networks |
title_fullStr |
Winsorization for Robust Bayesian Neural Networks |
title_full_unstemmed |
Winsorization for Robust Bayesian Neural Networks |
title_sort |
winsorization for robust bayesian neural networks |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/911e03cac136460fa0bfa8c32c4cbcc3 |
work_keys_str_mv |
AT somyasharma winsorizationforrobustbayesianneuralnetworks AT snigdhansuchatterjee winsorizationforrobustbayesianneuralnetworks |
_version_ |
1718412238468939776 |