Bayesian networks are graphical models that help understand and reason about complex systems with uncertainty using directed graphs. Skip ... Often uses opaque algorithms like neural networks, ...

The two main types are Bayesian models and neural networks. These two differ in their approach, but the goal remains the same: use patterns in data to separate legitimate from fraudulent transactions.

Graph neural networks are very powerful tools. They have already found powerful applications in domains such as route planning, fraud detection, network optimization, and drug research.

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, ...

A new method, physics-informed invertible neural networks (PI-INN), addresses Bayesian inverse problems by modeling parameter fields and solution functions. PI-INN achieves accurate posterior ...

M. Janssen et al, Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks, Astronomy & Astrophysics.

To overcome such inherent challenges with graph neural networks and improve recommendation abilities, LinkedIn has created a process it calls Performance-Adaptive Sampling Strategy (PASS). that ...