39 citations · 39 across the 1 of their papers we have counts for
5 papers
Reservoir Computing meets Recurrent Kernels and Structured Transforms
Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan +1
Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large…
Scalable spin-glass optical simulator
Davide Pierangeli, Mushegh Rafayelyan, Claudio Conti +1
Many developments in science and engineering depend on tackling complex optimizations on large scales. The challenge motivates intense search for specific computing hardware that t…
Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction
Mushegh Rafayelyan, Jonathan Dong, Yongqi Tan +2
Reservoir computing is a relatively recent computational paradigm that originates from a recurrent neural network and is known for its wide range of implementations using different…
Optical Reservoir Computing using multiple light scattering for chaotic systems prediction
Jonathan Dong, Mushegh Rafayelyan, Florent Krzakala +1
Reservoir Computing is a relatively recent computational framework based on a large Recurrent Neural Network with fixed weights. Many physical implementations of Reservoir Computin…
Ultra-broadband gradient-pitch Bragg-Berry mirrors
Mushegh Rafayelyan, Gonzague Agez, Etienne Brasselet
The realization of geometric phase optical device operating over a broad spectral range is usually confronted with intrinsic limitations depending of the physical process at play.…