Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing Data Abstract: Rapid advances in single-cell RNA sequencing (scRNA-seq) have made it possible to ...

Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural network’s latent representations into sparse, seemingly interpretable features. While these models have ...

Sparse autoencoders (SAE) use the concept of autoencoder with a slight modification. During the encoding phase, the SAE is forced to only activate a small number of the neurons in the intermediate ...

This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural ...

In this project, we employ an unsupervised process grounded in pre-trained Transformers-based Sequential Denoising Auto-Encoder (TSDAE), introduced by the Ubiquitous Knowledge Processing Lab of ...

Training a Variational Autoencoder Training a VAE involves two measures of similarity (or equivalently measures of loss). First, you must measure how closely the reconstructed output matches the ...

The second part of the autoencoder generates a cleaned version of the input. The first part of an autoencoder is called the encoder component, and the second part is called the decoder. To use an ...