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

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

Researchers have used machine learning to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the ...

Recentive’s four asserted patents involved software for generating event schedules and network maps using machine learning models trained on historical data. Although the applications had ...

Density functional theory is a widely used computer-based quantum mechanical method for calculating properties of atoms, molecules, and materials.

Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

To flexibly and robustly handle diverse problems, AI systems can leverage dual-process theories of human cognition that ...

But for Javier Orman the transition from professional violinist to a machine learning engineer at LinkedIn was a surprisingly natural one. Growing up in Montevideo, Uruguay, Orman excelled at both ...

This course is designed for students from various disciplines that use data to inform decision-making. It is suitable if you want an in-depth understanding of how machine learning can be integrated ...

The brain isn't a computer—it's a dynamic, adaptable system shaped by evolution. It's emotional, contextual, and designed for ...

Aims & ScopeNature Machine Intelligence publishes high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. We also explore and discuss the ...