Visual Studio Magazine

Data Anomaly Detection Using LightGBM

Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...
Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
IEEE

Rapidae: A Python Library for Creation, Experimentation, and Benchmarking of Autoencoder Models

Abstract: Autoencoders have proven successful across diverse applications such as data reconstruction, anomaly detection, and feature extraction, however, these advancements remain largely dispersed ...
C&EN

Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling

Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States ...
GitHub

Pytorch Variational Autoencoders

This reposistory is a collection of VAEs (Variational Autoencoder) implemented with PyTorch, which aim to provide a convenient and simple way to work around with available VAEs today (still updating).