Detecting malicious URLs using an autoencoder neural network. intrusion-detection anomalydetection malware-classifier anomaly-detection enriched-data malware-classification autoencoder-neural-network ...

A novel architecture and training strategy for graph neural networks (GNN). The proposed architecture, named as Autoencoder-Aided GNN (AA-GNN), compresses the convolutional features at multiple hidden ...

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 ...

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as ...

Abstract: This paper compares the performances of three types of autoencoder neural networks, namely, the traditional autoencoder with restricted Boltzmann machine (RBM), the stacked autoencoder ...

An autoencoder is a specific type of neural network. The main disadvantage of using a neural autoencoder is that you must fine-tune the training parameters (max epochs, learning rate, batch size) and ...

The Nature Index 2024 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their ...

Autoencoder neural network architecture. During the training, the autoencoder is provided with a series of images. The goal of the training is to find a way to tune the parameters in the encoder ...

A review from Xidian University shows that advanced computational algorithms—from neural networks and matrix methods to recommendation engines and ...