UCLA researchers demonstrate diffractive optical processors as universal nonlinear function approximators using linear ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear ...

1 MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom 2 Department of Psychology and York Biomedical Research Institute, University of York, York, United Kingdom ...

Can light perform millions of calculations at once without extra materials? A new optical system shows how this can be done.

Abstract: Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-and-error in the environment ...

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual ...

Abstract: Radial basis function neural networks (RBFNN) which are best suited for nonlinear function approximation, have been successfully applied to a wide range of areas including system modeling.