An autoencoder is a type of artificial neural network which can learn both linear and non-linear representations of the data, and use the learned representations to reconstruct the original data.

The new technology employs an autoencoder network and introduces an attention mechanism to address the challenges of insufficient data, cold starts and information overload that exist in ...

Lossy compression autoencoder for a covariance matrix with conditioning. Final project of Computing Methods for Experimental Physics course 2022/2023.

In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous techniques. Specifically, the proposed method uses a multilayer encoder to ...

The data that moves through an autoencoder isn’t just mapped straight from input to output, meaning that the network doesn’t just copy the input data. There are three components to an autoencoder: an ...

In this work, we propose an autoencoder (AE) neural network (NN)-based reduced model to accelerate such simulations. The AE NN is first trained to find a low-dimensional latent representation of the ...

The proposed model for network intrusion detection integrates a deep Autoencoder with LSTM-based sequence modeling. Initially, PCA reduces dimensionality of preprocessed network traffic features, ...