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 ...

Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). But, tools now exist to build accurate and sophisticated ...

Dr. Adi Hod. Cofounder & CEO at Velotix. Driven by a passion for data and cybernetic AI. Entrepreneur, professor, leader & innovator. AI is fast becoming embedded in industries, economies and ...

To that end, Dr Bhusan Chettri who earned his PhD in Machine Learning and AI for Voice Technology from QMUL, London described why there is a need for interpretability on today’s state-of-the-art ...

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 ...

Machine-learning algorithms can make mistakes and be difficult to use, so scientists at the Massachusetts Institute of Technology created explanation methods to assist users in understanding when ...

I am a computational biologist interested in interpretable machine learning for genomics and health care. Interpretable ...

Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). But, tools now exist to build accurate and sophisticated ...