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 biomimetic synapse built from water droplets and biological ion channels achieves synaptic plasticity and performs machine learning tasks.

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 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 ...

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 ...

In Neural Computation we apply computational and mathematical approaches to the study of the brain and, in the other direction, we seek to uncover insights into computation and mathematics by studying ...

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 ...