The concept of autoencoder was originally proposed by LeCun in 1987, early works on autoencoder were used for dimensionality reduction or feature learning. Recently, with the popularity of deep ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

lstm-neural-networks matlab-script anomaly-detection variational-autoencoder time-series-prediction encoder-decoder-architecture probability-distribution-function Updated on Jul 14, 2023 MATLAB ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

The autoencoder captures the salient features and removes the correlated features by mapping the input data to a high-dimensional space through the bottleneck structure. Figure 1 The structure of the ...

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 ...