Between the encoder and decoder, the autoencoder learns the feature representation of the data through a hidden layer. HOLO has innovated and optimized the stacked sparse autoencoder by utilizing the ...

The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels.

This repository contains the code for training and evaluating the models described in "Efficient protein structure generation with sparse denoising models": autoencoders are implemented in ...

Core Features A complete end-to-end pipeline from activation capture to Sparse AutoEncoder (SAE) training, feature interpretation, and verification, written in pure PyTorch with minimal dependencies.

To address these issues, we propose a Scale-adaptive Asymmetric Sparse Variational AutoEncoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which ...

A new research by Google DeepMind shows how sparse autoencoders (SAEs) with special JumpReLU activation can help interpret LLMs.

Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases.

Our proposed sparse autoencoder, and the deep-network is trained simultaneously for feature selection and improving classifier decision. Any further training to improve the classifier was done by ...