Turning uncertainty into a design tool for AI-engineered molecules

While precision seems critical for science, researchers from the U.S. Department of Energy's (DOE) Brookhaven National ...
IEEE

A Variational Bayesian Two-Phase Adaptive Prior Kalman Filter With Unknown Noise Covariances

Abstract: This article addresses the issue of online state estimation when the covariances of the process and measurement noise (PMN) are unknown. In practical scenarios, inaccurate PMN covariances ...
Frontiers

Generative Models of Neural Activity: Using AI to Simulate, Interpret, and Predict Brain Dynamics

The advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...
IEEE

Satellite Image Time-Series Data Augmentation Using an Attention Mechanism Variational Recurrent Autoencoder

Abstract: Data scarcity presents a significant challenge in satellite image analysis, particularly for developing robust models in remote sensing applications. High-quality and abundant data are ...