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This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines energy minimization, homeostatic control, and nudging towards the correct response.
Cxvii. some general theorems for non-linear systems possessing reactance
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Cxvi. some general theorems for non-linear systems possessing resistance
W. Millar · 1951
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Criteria for robust stability in a class of lateral inhibition networks coupled through resistive grids
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L. Bottou · 2010
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Binaryconnect: Training deep neural networks with binary weights during propagations
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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T. Mesnard, W. Gerstner, and J. Brea · 2016
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Mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse
C.-C. Chang, P.-C. Chen, T. Chou, I.-T. Wang, B. Hudec, C.-C. Chang, C.-M. Tsai, T.-S. Chang, and T.-H. Hou · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
B. Scellier and Y. Bengio · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Equivalent-accuracy accelerated neural-network training using analogue memory
S. Ambrogio, P. Narayanan, H. Tsai, R. M. Shelby, I. Boybat, C. Di Nolfo, S. Sidler, M. Giordano, M. Bodini, N. C. Farinha, et al · 2018
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Updates of equilibrium prop match gradients of backprop through time in an rnn with static input
M. Ernoult, J. Grollier, D. Querlioz, Y. Bengio, and B. Scellier · 2019
Cited alongside, same era.
Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
A. Laborieux, M. Ernoult, B. Scellier, Y. Bengio, J. Grollier, and D. Querlioz · 2021
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Eqspike: spike-driven equilibrium propagation for neuromorphic implementations
E. Martin, M. Ernoult, J. Laydevant, S. Li, D. Querlioz, T. Petrisor, and J. Grollier · 2021
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A deep learning theory for neural networks grounded in physics
B. Scellier · 2021
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Supervised learning in physical networks: From machine learning to learning machines
M. Stern, D. Hexner, J. W. Rocks, and A. J. Liu · 2021
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Visualizing a joint future of neuroscience and neuromorphic engineering
F. Zenke, S. M. Bohté, C. Clopath, I. M. Comşa, J. Göltz, W. Maass, T. Masquelier, R. Naud, E. O. Neftci, M. A. Petrovici, et al · 2021
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Training a spiking neural network with equilibrium propagation
P. O’Connor, E. Gavves, and M. Welling · 2019
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Memristive crossbar arrays for brain-inspired computing
Q. Xia and J. J. Yang · 2019
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Equilibrium propagation with continual weight updates
M. Ernoult, J. Grollier, D. Querlioz, Y. Bengio, and B. Scellier · 2020
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Training end-to-end analog neural networks with equilibrium propagation
J. Kendall, R. Pantone, K. Manickavasagam, Y. Bengio, and B. Scellier · 2020
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Demonstration of decentralized, physics-driven learning
S. Dillavou, M. Stern, A. J. Liu, and D. J. Durian · 2021
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N. Zucchet, S. Schug, J. von Oswald, D. Zhao, and J. Sacramento · 2021
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Learning by non-interfering feedback chemical signaling in physical networks
V. R. Anisetti, B. Scellier, and J. Schwarz · 2022
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Deep physical neural networks trained with backpropagation
L. G. Wright, T. Onodera, M. M. Stein, T. Wang, D. T. Schachter, Z. Hu, and P. L. McMahon · 2022
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Beyond backpropagation: implicit gradients for bilevel optimization
N. Zucchet and J. Sacramento · 2022
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