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 ...

To address this issue, we proposed an intelligent HB design method based on the autoencoder (AE) neural network in this paper. By mapping the HB system to an AE neural network, the solving 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 ...

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 ...