Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning

Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning

Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly for large datasets. Despite its advantages in handling large...

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Bibliographic Details
Main Authors:	Abdullah A. Abdullah, Masoud M. Hassan, Yaseen T. Mustafa
Format:	Article
Language:	English
Published:	MDPI AG 2023-04-01
Series:	Applied Sciences
Subjects:	uncertainty quantification Bayesian deep learning MLP-Mixer variational inference (VI) MC-dropout
Online Access:	https://www.mdpi.com/2076-3417/13/7/4547

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