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 ...

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 ...

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 ...

AI-driven genomic analysis could help researchers identify existing drugs that may be repurposed for subtype-specific breast cancer treatment. The review proposes an interpretability-driven framework ...

Find out how this structured machine learning roadmap called I-Con could lead to breakthroughs in AI. A new “periodic table for machine learning” is reshaping how researchers explore AI, unlocking ...

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 ...

Machine learning is changing the front end of drug discovery, where researchers decide which targets to pursue and which molecules deserve costly laboratory work. Its deeper test lies further ...