Machine learning-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the ...

Federated learning (FL) has emerged as a popular machine learning paradigm which allows multiple data owners to train models collaboratively with out sharing their raw datasets. It holds potential for ...

Talking to yourself feels deeply human. Inner speech helps you plan, reflect, and solve problems without saying a word.

SANTA CLARA, Calif.--(BUSINESS WIRE)--What’s New: Today, Intel and the National Science Foundation (NSF) announced award recipients of joint funding for research into the development of future ...

EPFL researchers have developed a machine learning approach to compressing image data with greater accuracy than learning-free computation methods, with applications for retinal implants and other ...

A Cornell research group led by Prof. Peter McMahon, applied and engineering physics,has successfully trained various physical systems to perform machine learning computations in the same way as a ...

A major challenge to developing better neural prostheses is sensory encoding: transforming information captured from the environment by sensors into neural signals that can be interpreted by the ...

A new technical paper titled “Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor” was published by researchers at UIUC and Microsoft Research. “As AI chips ...