Deep optical learning devices and architectures
Kelvin H. Wagner · 2016
The innovations of neuron functionality and network architecture that have allowed the convergence of back-propagating deep-learning algorithms for large problems, such as rectifying linear units, dropout, and convolutional layers, may make the implementation of optical neurons and multilayer adaptive holographic learning networks feasible.