Tomorrow's AI services depend on networks built for massive inference growth.

Abstract: The Bayesian network (BN) method has been identified as a research hotspot in dynamic risk assessment (DRA) for systems. The traditional BN inference process relies on crisp probabilities; ...

Abstract: Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) ...

AI has helped astronomers crack open some of the universe s best-kept secrets by analyzing massive datasets about black holes. Using over 12 million simulations powered by high-throughput computing, ...

Understanding the interplay between network architecture, dataset statistics, and learning algorithms is a key challenge in deep learning. We overcome this challenge analytically for zero-noise ...

Data from human subjects as well as animals show that working memories are associated with a sense of uncertainty. Indeed, a sense of uncertainty is what allows an observer to properly weigh new ...

Directed acyclic graphs or Bayesian networks that are popular in many AI-related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of ...

Don’t worry, a little Bayesian analysis won’t hurt you. By Siobhan Roberts There is a statistician’s rejoinder — sometimes offered as wry criticism, sometimes as honest advice — that could hardly be a ...

Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks ...