Efficient variational Bayesian neural network ensembles for outlier\n detection

2017/03/20 by Nick Pawlowski, Pawlowski, Nick, Miguel Jaques +3
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1703.06749

openalex publication_date 2017/03/20 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

Abstract

In this work we perform outlier detection using ensembles of neural networks\nobtained by variational approximation of the posterior in a Bayesian neural\nnetwork setting. The variational parameters are obtained by sampling from the\ntrue posterior by gradient descent. We show our outlier detection results are\ncomparable to those obtained using other efficient ensembling methods.\n

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