The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

Machines can learn not only to make predictions, but also to handle causal relationships. An international research team shows how this could make therapies safer, more efficient, and more ...

With the emergence of huge amounts of heterogeneous multi-modal data, including images, videos, texts/languages, audios, and multi-sensor data, deep learning-based methods have shown promising ...

The surge in enterprise AI has fueled interest in causal analysis. In this piece, I explore the threads that bind cause and effect - and how they can be applied across a range of industry scenarios.

Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, ...

The manufacturing landscape is evolving rapidly, with intelligent systems increasingly promising to boost efficiency, quality, and overall competitiveness. Traditional machine learning (ML) has ...

Recent advances in artificial intelligence (AI) and machine learning (ML) have transformed our ability to decode complex ...

Potential treatments for amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases may already be out there in the form of drugs prescribed for other conditions. A team of researchers ...

SAN FRANCISCO--(BUSINESS WIRE)--Today MLCommons™, an open engineering consortium, released new results for three MLPerf™ benchmark suites - Inference v2.0, Mobile v2.0, and Tiny v0.7. These three ...