Dimensionality reduction is a technique that reduces the number of features in a dataset without losing much information. It can help improve the performance and efficiency of machine learning ...

Below, we describe each type in detail, including their specific architectures and diagrams. The Fully Connected Autoencoder ... making it more suitable for image data like FashionMNIST. The Sparse ...

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

We propose a natural autoencoder that maps all the discrete and overlapping sparse features for each SCFG rule into a continuous vector, so that the information encoded in sparse feature vectors ...

In this paper, we propose the Uncorrelated Sparse Autoencoder with Long Short-Term Memory (USAL). USAL is a novel neural network that addresses the challenging task of long-term SOC estimation given a ...

This repository includes the implentation of the hyperspectral image (HSI) band selection method in SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image ...