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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.
Nonlinear analysis of hydrodynamic instability in laminar flames—I. Derivation of basic equations
GI Sivashinsky · 1977
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Diffusion-induced chaos in reaction systems
Yoshiki Kuramoto · 1978
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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note
Herbert Jaeger · 2001
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Concentration inequalities
Stéphane Boucheron, Gábor Lugosi, and Olivier Bousquet · 2003
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An experimental unification of reservoir computing methods
David Verstraeten, Benjamin Schrauwen, Michiel d’Haene, and Dirk Stroobandt · 2007
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
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Reservoir computing approaches to recurrent neural network training
Mantas Lukoševičius and Herbert Jaeger · 2009
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2009
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On computational power and the order-chaos phase transition in reservoir computing
Benjamin Schrauwen, Lars Büsing, and Robert A Legenstein · 2009
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Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing
Laurent Larger, Miguel C Soriano, Daniel Brunner, Lennert Appeltant, Jose M Gutiérrez, Luis Pesquera, Claudio R Mirasso, and Ingo Fischer · 2012
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All-optical reservoir computing
François Duport, Bendix Schneider, Anteo Smerieri, Marc Haelterman, and Serge Massar · 2012
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Reservoir computing trends
Mantas Lukoševičius, Herbert Jaeger, and Benjamin Schrauwen · 2012
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Recurrent kernel machines: Computing with infinite echo state networks
Michiel Hermans and Benjamin Schrauwen · 2012
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Random feature maps for dot product kernels
Purushottam Kar and Harish Karnick · 2012
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Real-time parallel processing of grammatical structure in the fronto-striatal system: A recurrent network simulation study using reservoir computing
Xavier Hinaut and Peter Ford Dominey · 2013
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Fastfood-computing Hilbert space expansions in loglinear time
Quoc Le, Tamás Sarlós, and Alexander Smola · 2013
Cited alongside, same era.
FPGA implementation of reservoir computing with online learning
Piotr Antonik, Anteo Smerieri, François Duport, Marc Haelterman, and Serge Massar · 2015
Cited alongside, same era.
General-purpose LSM learning processor architecture and theoretically guided design space exploration
Qian Wang, Yingyezhe Jin, and Peng Li · 2015
Cited alongside, same era.
A digital liquid state machine with biologically inspired learning and its application to speech recognition
Yong Zhang, Peng Li, Yingyezhe Jin, and Yoonsuck Choe · 2015
Cited alongside, same era.
ACDC: A structured efficient linear layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard, and Nando de Freitas · 2015
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Reservoir computing with untrained convolutional neural networks for image recognition
Zhiqiang Tong and Gouhei Tanaka · 2018
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Learning with SGD and random features
Luigi Carratino, Alessandro Rudi, and Lorenzo Rosasco · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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On the spectrum of random features maps of high dimensional data
Zhenyu Liao and Romain Couillet · 2018
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Learning compressed transforms with low displacement rank
Anna Thomas, Albert Gu, Tri Dao, Atri Rudra, and Christopher Ré · 2018
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Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography
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Random projections through multiple optical scattering: Approximating kernels at the speed of light
Alaa Saade, Francesco Caltagirone, Igor Carron, Laurent Daudet, Angélique Drémeau, Sylvain Gigan, and Florent Krzakala · 2016
Cited alongside, same era.
Orthogonal random features
Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, and Sanjiv Kumar · 2016
Cited alongside, same era.
Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
Cited alongside, same era.
A local echo state property through the largest lyapunov exponent
Gilles Wainrib and Mathieu N Galtier · 2016
Cited alongside, same era.
Recent advances in recurrent neural networks
Hojjat Salehinejad, Sharan Sankar, Joseph Barfett, Errol Colak, and Shahrokh Valaee · 2017
Cited alongside, same era.
Performance and robustness of bio-inspired digital liquid state machines: A case study of speech recognition
Yingyezhe Jin and Peng Li · 2017
Cited alongside, same era.
Generalization properties of learning with random features
Alessandro Rudi and Lorenzo Rosasco · 2017
Cited alongside, same era.
Piotr Antonik, Marvyn Gulina, Jaël Pauwels, and Serge Massar · 2018
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Optical reservoir computing using multiple light scattering for chaotic systems prediction
Jonathan Dong, Mushegh Rafayelyan, Florent Krzakala, and Sylvain Gigan · 2019
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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, and Akira Hirose · 2019
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The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei and Andrea Montanari · 2019
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Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Kevin Liang, Guoyin Wang, Yitong Li, Ricardo Henao, and Lawrence Carin · 2019
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Recurrent Kernel Networks
Dexiong Chen, Laurent Jacob, and Julien Mairal · 2019
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Pantelis R Vlachas, Jaideep Pathak, Brian R Hunt, Themistoklis P Sapsis, Michelle Girvan, Edward Ott, and Petros Koumoutsakos · 2019
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Reinforcement learning with convolutional reservoir computing
Hanten Chang and Katsuya Futagami · 2020
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Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond
Fanghui Liu, Xiaolin Huang, Yudong Chen, and Johan AK Suykens · 2020
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Kernel computations from large-scale random features obtained by optical processing units
Ruben Ohana, Jonas Wacker, Jonathan Dong, Sébastien Marmin, Florent Krzakala, Maurizio Filippone, and Laurent Daudet · 2020
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Deep Randomized Neural Networks
Claudio Gallicchio and Simone Scardapane · 2020
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