Birdsong Sequential Autoencoder A research-grade Python package for LFADS-style sequential autoencoder analysis of birdsong syntax and neural dynamics. This package provides comprehensive tools for ...

05_autoencoder.ipynb - This script prepares the data to be used as input for the autoencoder, implements the autoencoder architecture, uses it to group the data together with the K-Means algorithm, ...

In this article, the authors discuss how to detect fraud in credit card transactions, using Random Forest, Logistic Regression, Isolation Forest and Neural Autoencoder.

In the last decade, automatic writer identification using a convolutional neural network (CNN) has been well studied. For further performance improvement of the writer identification task, a ...

However, relatively little research has addressed open-set learning issues involving unknown working modes. A multifunction radar working mode open-set recognition method based on dual autoencoder ...

The autoencoder is an unsupervised deep neural network that learns a compressed representation from the input data and reconstructs an output that is as similar as possible to the original data.

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 ...