The autoencoder is trained by minimizing the reconstruction error, which is the difference between the input and ... autoencoders have been applied in deep learning, like pretraining deep neural ...

Autoencoders (AEs) are unsupervised learning models that automatically extract data features from large datasets. With advancements in deep ... autoencoder (WAE): The WAE introduces the Wasserstein ...

In order to eliminate the dimensional influence between different features ... is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful ...

In this paper, we propose a novel semi -supervised distributed approach based on stacked denoising sparse autoencoder and SVM for large-scale intrusion detection systems. Our aim is to explore the ...

Abstract: This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked ...