A new technical paper titled “Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks” was published by researchers at Université Grenoble Alpes, CEA, ...

When a cancer returns or spreads after surgery, radiation, and chemotherapy, the therapeutic arsenal shrinks dramatically.

IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...

Bayesian networks have become popular tools for enterprise data scientists working with prediction, as the rise of cheap and abundant cloud computing has made way for adaptable infrastructure. In the ...

Gut bacteria are known to be a key factor in many health-related concerns. However, the number and variety of them is vast, as are the ways in which they interact with the body's chemistry and each ...

Towards Reinforcement Learning-based Flow Space OptimizationA Deep Paradigm Shift in Modern Bayesian Inference — From the Limits of MCMC/Variational Inference to Neural Processes and GFlowNetsIntroduc ...

According to GoogleDeepMind, Zoubin Ghahramani explains how uncertainty and probability make real‑world AI decisions safer and more reliable.