Interpretability in deep learning for finance: A case study for the Heston model

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
insideHPC

Machine Learning Interpretability with Driverless AI

Data visualization techniques for representing high-degree interactions and nuanced data structures. Contemporary linear model variants that incorporate machine learning and are appropriate for use in ...
EurekAlert!

Machine learning-driven predictions of biochar yield and NPK composition: insights into biomass pyrolysis with data augmentation and model interpretability (IMAGE)

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert ...
Quanta Magazine

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...
Opinion
2UrbanGirls on MSNOpinion

Neel Somani on formal methods and the future of machine learning safety

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...