Parallel photonic information processing at gigabyte per second data rates using transient states
TLDR
The potential of a simple photonic architecture to process information at unprecedented data rates is demonstrated, implementing a learning-based approach and all digits with very low classification errors are identified and chaotic time-series prediction with 10% error is performed.Abstract:
Inspired by neural networks, reservoir computing uses nonlinear transient states to perform computations, offering faster parallel information processing Brunner et al show a photonic approach to reservoir computing capable of simultaneous spoken digit and speaker recognition at high data ratesread more
Citations
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Quantum machine learning
Jacob Biamonte,Jacob Biamonte,Peter Wittek,Nicola Pancotti,Patrick Rebentrost,Nathan Wiebe,Seth Lloyd +6 more
TL;DR: The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
Journal ArticleDOI
All-optical machine learning using diffractive deep neural networks
TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Journal ArticleDOI
Recent advances in physical reservoir computing: A review
Gouhei Tanaka,Toshiyuki Yamane,Jean Benoit Héroux,Ryosho Nakane,Naoki Kanazawa,Seiji Takeda,Hidetoshi Numata,Daiju Nakano,Akira Hirose +8 more
TL;DR: An overview of recent advances in physical reservoir computing is provided by classifying them according to the type of the reservoir to expand its practical applications and develop next-generation machine learning systems.
Journal ArticleDOI
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
TL;DR: The effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution is demonstrated.
Journal ArticleDOI
All-optical spiking neurosynaptic networks with self-learning capabilities.
TL;DR: An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
References
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The Case Study
TL;DR: On May 25, 1977, IEEE member, Virginia Edgerton, a senior information scientist employed by the City of New York, telephoned the chairman of CSIT's Working Group on Ethics and Employment Practices, having been referred to the committee by IEEE Headquarters.
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Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Herbert Jaeger,Harald Haas +1 more
TL;DR: A method for learning nonlinear systems, echo state networks (ESNs), which employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains is presented.
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Time Series Prediction: Forecasting The Future And Understanding The Past
TL;DR: By reading time series prediction forecasting the future and understanding the past, you can take more advantages with limited budget.
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Information processing using a single dynamical node as complex system
Lennert Appeltant,Miguel C. Soriano,G. Van der Sande,Jan Danckaert,Serge Massar,Joni Dambre,Benjamin Schrauwen,Claudio R. Mirasso,Ingo Fischer +8 more
TL;DR: This work introduces a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback and proves that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing.
Journal ArticleDOI
Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing.
Laurent Larger,Miguel C. Soriano,Daniel Brunner,Lennert Appeltant,José M. Gutiérrez,Luis Pesquera,Claudio R. Mirasso,Ingo Fischer +7 more
TL;DR: This work experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback and implements a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities.
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Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
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