Scientific Research Publishing

A Hybrid CNN-LSTM Variational Autoencoder for Treatment Response Prediction in Synthetic Psychiatric Data ()

Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of conventional ...
GitHub

A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder (AVE)

This repository contains the source code, scripts, and supplementary materials for the paper: "A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder ...
FierceBiotech

Merck inks $349M biobucks deal to apply AI to challenging targets

Merck & Co. has doubled down on its partnership with Variational AI, striking a deal worth up to $349 million to collaborate on small molecule candidates against two targets. Variational disclosed a ...
IEEE

A Novel Hybrid Signal Processing Based Deep Learning Method for Cyber-Physical Resilient Harbor Integrated Shipboard Microgrids

Abstract: Recently, the cybersecurity of harbor-integrated shipboard microgrids (SMGs) has become a significant concern for ensuring the integrity and resilience of maritime energy systems within ...
Business Wire

Variational AI Announces Oversubscribed $5.5 Million Financing to Launch Foundation Model for Small Molecule Drug Discovery

VANCOUVER, British Columbia--(BUSINESS WIRE)--Variational AI, the company behind Enki™, an advanced foundation model for small molecule drug discovery, today ...
IEEE

Anomaly Detection In Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning

Abstract: A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional ...
Oil

ML, hybrid risk models predict stuck pipe

Drilling incident predictors (DIP) anticipate drilling incidents which can lead to non-productive time. Researchers at the University of Texas at Austin produced data-driven and hybrid data-physics ...