Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To ...

The script helps to train your own Deep Autoencoder with Extreme Learning Machines. It performs a Deep Autoencoder model with with a specified model. After that, it utilizes both Neural Networks and ...

Clone the repository Note: Repository may be quite large as results/ directory has results that contains the deep learning model and the images and graphs of the results Remarks: Knowledge of the ...

In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs ...

The overview of our model is shown in Figure 1. The bottleneck of the sparse autoencoder is used as input vector to the deep neural network. In the figure, neurons labeled as (+1) are the bias units ...

SHENZHEN, China, Feb. 14, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they Announced the ...

the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, ...