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Deep Reservoir Computing has emerged as a new paradigm for deep learning, which is based around the reservoir computing principle of maintaining random pools of neurons combined with hierarchical deep learning.
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note
H. Jaeger · 2001
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A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
R. Legenstein, D. Pecevski, and W. Maass · 2008
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Hierarchical computation in the canonical auditory cortical circuit
C. A. Atencio, T. O. Sharpee, and C. E. Schreiner · 2009
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Reservoir computing approaches to recurrent neural network training
M. Lukoševičius and H. Jaeger · 2009
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2010
Cited alongside, same era.
Liquid state machines: motivation, theory, and applications
W. Maass · 2011
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Cited alongside, same era.
Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
N. Frémaux and W. Gerstner · 2016
Cited alongside, same era.
Random synaptic feedback weights support error backpropagation for deep learning
T. P. Lillicrap, D. Cownden, D. B. Tweed, and C. J. Akerman · 2016
Cited alongside, same era.
Deep echo state network (deepesn): A brief survey
C. Gallicchio and A. Micheli · 2017
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Deep-esn: A multiple projection-encoding hierarchical reservoir computing framework
Q. Ma, L. Shen, and G. W. Cottrell · 2017
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The uea multivariate time series classification archive, 2018
A. Bagnall, H. A. Dau, J. Lines, M. Flynn, J. Large, A. Bostrom, P. Southam, and E. Keogh · 2018
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Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach
J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott · 2018
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Reservoir computing with untrained convolutional neural networks for image recognition
Z. Tong and G. Tanaka · 2018
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Direct feedback alignment provides learning in deep neural networks
A. Nøkland · 2016
Cited alongside, same era.
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Learning to solve the credit assignment problem
B. J. Lansdell, P. R. Prakash, and K. P. Kording · 2019
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