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 ...
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 ...
Reuters

Dan Herbatschek Leads New Initiative in New York and Los Angeles to Advance Transparency and Interpretability in Machine Learning

NEW YORK, NY, November 28, 2025 (EZ Newswire) -- Dan Herbatschek, opens new tab, founder and CEO of Ramsey Theory Group, opens new tab, has announced a new initiative to strengthen transparency, ...
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 ...
mccormick.northwestern.edu

Trust in Numbers

William Chiu (MSiA '13) works in the fast-paced world of finance, where algorithms often make decisions that impact millions of lives and even more dollars. Now he's helping MLDS students develop ...
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 ...
EurekAlert!

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

CNN architecture summary: The first dimension in all the layers “?” refers to the batch size. It is left as an unknown or unspecified variable within the network architecture so that it can be chosen ...