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

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) ...

Abstract: Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical ...

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 ...