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Equilibrium Propagation (EP) is a biologically inspired learning algorithm for convergent recurrent neural networks, i.e.
A learning algorithm for boltzmann machines
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski · 1985
Earlier work this paper cites.
Learning internal representations by error propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1985
Earlier work this paper cites.
A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
L. B. Almeida · 1987
Earlier work this paper cites.
Generalization of back-propagation to recurrent neural networks
F. J. Pineda · 1987
Earlier work this paper cites.
The recent excitement about neural networks
F. Crick · 1989
Earlier work this paper cites.
Contrastive hebbian learning in the continuous hopfield model
J. R. Movellan · 1991
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
B. Scellier and Y. Bengio · 2017
Cited alongside, same era.
Neuromorphic computing with nanoscale spintronic oscillators
J. Torrejon, M. Riou, F. A. Araujo, S. Tsunegi, G. Khalsa, D. Querlioz, P. Bortolotti, V. Cros, K. Yakushiji, A. Fukushima, et al · 2017
Cited alongside, same era.
Equivalent-accuracy accelerated neural-network training using analogue memory
S. Ambrogio, P. Narayanan, H. Tsai, R. M. Shelby, I. Boybat, C. Nolfo, S. Sidler, M. Giordano, M. Bodini, N. C. Farinha, et al · 2018
Cited alongside, same era.
Big data needs a hardware revolution
Editorial · 2018
Cited alongside, same era.
Initialized equilibrium propagation for backprop-free training
P. O’Connor, E. Gavves, and M. Welling · 2018
Later among the works it cites.
Vowel recognition with four coupled spin-torque nano-oscillators
M. Romera, P. Talatchian, S. Tsunegi, F. A. Araujo, V. Cros, P. Bortolotti, J. Trastoy, K. Yakushiji, A. Fukushima, H. Kubota, et al · 2018
Later among the works it cites.
Generalization of equilibrium propagation to vector field dynamics
B. Scellier, A. Goyal, J. Binas, T. Mesnard, and Y. Bengio · 2018
Later among the works it cites.
All-optical spiking neurosynaptic networks with self-learning capabilities
J. Feldmann, N. Youngblood, C. Wright, H. Bhaskaran, and W. Pernice · 2019
Closest in time.
Training a spiking neural network with equilibrium propagation
P. O’Connor, E. Gavves, and M. Welling · 2019
Closest in time.
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B. Scellier and Y. Bengio · 2019
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