Creating machine learning models that generate accurate results is one thing. But ensuring model interpretability -- meaning the ability to understand why the model generates certain results -- is ...

Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL ...

With machine learning on the rise, businesses are relying on machine learning models and algorithms to derive insights from data and make predictions. Serg Masís, data scientist and author of ...

Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical ...

Machine learning is taking the world by storm, helping automate more and more tasks. As digital transformation expands, the volume and coverage of available data grows, and machine learning sets its ...

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...

Neural networks are famously incomprehensible — a computer can come up with a good answer, but not be able to explain what led to the conclusion. Been Kim is developing a “translator for humans” so ...

Machine learning is accelerating target identification, toxicity prediction and drug repurposing, but researchers insist ...

Anyone who runs a business knows that one of the hardest things to do is accuse a customer of malfeasance. That’s why, before members of Scandinavian Airlines’ (SAS) fraud detection unit accuse a ...

Machine learning is ubiquitous: it is involved in detecting spam emails, flagging fraudulent purchases, and providing the next movie in a Netflix binge. But few users at the mercy of machine learning ...