GitHub

Neuromechanics-Lab/motor-module-autoencoder

In order to use this package, please make sure that you have access to a GPU enabled runtime. This can be done easily with a conda environment, with the following ...
GitHub

latent_diffusion_mnist_ddpm_using_autoencoder.py

In this notebook, we will implement an **Autoencoder** with Convolutional Attention Blocks (CABs) to encode and decode MNIST digits, aiming to learn efficient latent representations. To get started, ...
IEEE

Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection

Abstract: The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
IEEE

Anomaly Detection in Complex Systems Using Extended Isolation Forest and Denoising Autoencoder

Abstract: In this paper, we propose an anomaly detection model based on Extended Isolation Forest and Denoising Autoencoder, which achieves unsupervised anomaly detection with good generalization ...
marktechpost

This AI Paper Introduces a Short KL+MSE Fine-Tuning Strategy: A Low-Cost Alternative to End-to-End Sparse Autoencoder Training for Interpretability

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...