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 ...

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 ...

A new theoretical framework argues that the long-standing split between computational functionalism and biological naturalism misses how real brains actually compute.

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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 ...

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 ...

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 ...

Unsupervised, model-free method preserves key data better than traditional statistical techniques for next generation cognitive ML for multi-modal data. These methods prove the utility of algorithmic ...

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 ...