Search Results for author:
Found 5 papers, 3 papers with code
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty.
Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds
The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented.
Identifying the Causes of Pyrocumulonimbus (PyroCb)
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented.
The Unreasonable Effectiveness of Deep Evidential Regression
There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains.
Multivariate Deep Evidential Regression
We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
