These methods belong to the field of machine learning, however there are also many models ... In the following sections, we will apply three different autoencoders which are simple autoencoder, deep ...

By introducing the dimension reduction layer in the autoencoder structure based on deep learning, we can extend the current DNN framework into a more generalized one to enhance the generality of OOD.

The script helps to train your own Deep Autoencoder with Extreme Learning Machines. It performs a Deep Autoencoder model with with a specified model. After that, it utilizes both Neural Networks and ...

In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to ...

This paper introduces a novel hybrid Deep Learning framework that employs an Autoencoder-based feature extraction with a stacked Ensemble learning approach for enhancing classification performance.

Therefore, we propose a novel malware detection model in this paper. This model combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the ...

This application of the "Variational AutoEncoder" deep-learning model is an example of how machine learning can be used to interpret and extract meaning from difficult data sets that are too ...

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