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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of ...

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 ...

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 ...