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 ...
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 ...
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 ...
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 ...
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