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Found 8 papers, 1 papers with code
Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing
Time delay reservoir computing (TDRC) using semiconductor lasers (SLs) has proven to be a promising photonic analog approach for information processing.
Learning unseen coexisting attractors
Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system.
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
We train these networks to predict the dynamics of delay-dynamical and spatio-temporal systems for a single size.
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing
Reservoir computers are powerful tools for chaotic time series prediction.
56 GBaud PAM-4 100 km Transmission System with Photonic Processing Schemes
In this work, we show that the effectiveness of the internal fading memory depends significantly on the properties of the signal to be processed.
Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops.
Reinforcement Learning in a large scale photonic Recurrent Neural Network
Realizing photonic Neural Networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware was lacking so far.
Photonic Delay Systems as Machine Learning Implementations
We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach.
