ArticleEntropy (Basel, Switzerland)2021
Winsorization for Robust Bayesian Neural Networks.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Vehicle CO₂ emission prediction based on firefly with ant colony optimization tuned long short-term memory model.Scientific reports · 2026Article
- Deep Learning Parameter Estimation and Quantum Control of Single Molecules.ACS physical chemistry Au · 2026Article
- Associations of urinary bisphenol A and its emerging substitutes with subfecundity in preconception couples: a prospective nested case-control study.Environmental health : a global access science source · 2026Article
- Estimating chemical oxygen demand in municipal landfill leachate using multilayer perceptron artificial neural networks based on seasonal monitoring data.Scientific reports · 2026Article
- The Influence of Age on Notch1 and VEGF-A Responses During Exercise.Journal of aging research · 2026Article
- Sex Differences in Behavioral and Psychopathological Trajectories From Late Childhood to Early Adolescence: Implications for Suicidality Risk.Depression and anxiety · 2025Article
- Development and validation of a web-based predictive model for preoperative diagnosis of localized colorectal cancer and colorectal adenoma.Frontiers in oncology · 2023Article
- Machine Learning Methods for Multiscale Physics and Urban Engineering Problems.Entropy (Basel, Switzerland) · 2022Article
- Cardiovascular adjustments to cold pressor test in postmenopausal women and the impact of α1-adrenergic blockade.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.