ABSTRACT: Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective ...

Abstract: Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of ...

A comprehensive Edge AI and IoT anomaly detection system designed for research and educational purposes. This project demonstrates real-time anomaly detection using autoencoder-based models optimized ...

Project Z-Code is a component of Microsoft’s larger XYZ-code initiative to combine AI models for text, vision, audio, and language. Z-code supports the creation of AI systems that can speak, see, hear ...

Abstract: This project introduces an innovative method for enhancing license plate images by employing a blind autoencoder-based denoising and deblurring technique. Unlike conventional approaches that ...

Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (article link) by Antoine Caillon and Philippe Esling. If you use RAVE as a part of a music ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...

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 ...

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the ...

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational ...