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 ...

This project implements a basic autoencoder with the following features: Uses the MNIST dataset (handwritten digit images). The autoencoder is designed with sigmoid activations to compress the images ...

Here I create two Juypter notebooks, one for KAN-based AutoEncoder and another for MLP-based AutoEncoder. My toy example shows that KAN is way better than MLP in representing sinusoidal signals, which ...

Thus, we first used the autoencoder network, to roughly screen out the features to a proper dimension. The detailed two-stepwise feature selection procedure is described as follows: Step 1: We trained ...

Training a Variational Autoencoder Training a VAE involves two measures of similarity (or equivalently measures of loss). First, you must measure how closely the reconstructed output matches the ...

And there are specialized techniques for working with specific types of data, such as fraud detection systems. That said, applying a neural autoencoder anomaly detection system to tabular data is ...