Benjamin Scellier
Title
Cited by
Cited by
Year
Equilibrium propagation: bridging the gap between energy-based models and backpropagation
B Scellier, Y Bengio
Frontiers in computational neuroscience 11, 24, 2017
1832017
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
1692019
Towards a biologically plausible backprop
B Scellier, Y Bengio
arXiv preprint arXiv:1602.05179 914, 2016
362016
Generalization of Equilibrium Propagation to Vector Field Dynamics
B Scellier, A Goyal, J Binas, T Mesnard, Y Bengio
arXiv preprint arXiv:1808.04873, 2018
22*2018
Equivalence of equilibrium propagation and recurrent backpropagation
B Scellier, Y Bengio
Neural computation 31 (2), 312-329, 2019
202019
Feedforward initialization for fast inference of deep generative networks is biologically plausible
Y Bengio, B Scellier, O Bilaniuk, J Sacramento, W Senn
arXiv preprint arXiv:1606.01651, 2016
132016
Training End-to-End Analog Neural Networks with Equilibrium Propagation
J Kendall, R Pantone, K Manickavasagam, Y Bengio, B Scellier
arXiv preprint arXiv:2006.01981, 2020
92020
Updates of equilibrium prop match gradients of backprop through time in an rnn with static input
M Ernoult, J Grollier, D Querlioz, Y Bengio, B Scellier
Advances in Neural Information Processing Systems 32, 7081-7091, 2019
82019
Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
A Laborieux, M Ernoult, B Scellier, Y Bengio, J Grollier, D Querlioz
Frontiers in Neuroscience 15, 129, 2021
72021
Equilibrium Propagation with Continual Weight Updates
M Ernoult, J Grollier, D Querlioz, Y Bengio, B Scellier
arXiv preprint arXiv:2005.04168, 2020
52020
A deep learning theory for neural networks grounded in physics
B Scellier
arXiv preprint arXiv:2103.09985, 2021
12021
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Articles 1–11