Our research team, led by Michalis Michaelides, have penned a great perspective on adding quantified uncertainties to Artificial Neural Network (ANN) models.
In the world of decision-making based on model estimates, understanding and quantifying uncertainty is key. It not only helps us optimize more effectively but also lays the groundwork for principled decision-making.In our latest piece, we break down some easily accessible routes to incorporate uncertainty quantification into ANNs.
Dive into the details and explore the broader landscape if you're keen to delve deeper into this intriguing topic.
Click here to read the full article and click here to read the article on Medium.