1.2k citations · 8.7k across the 113 of their papers we have counts for
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Unsupervised End-to-End Training with a Self-Defined Target
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy +2
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phase…
Training an Ising Machine with Equilibrium Propagation
Jérémie Laydevant, Danijela Markovic, Julie Grollier
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins…
Training Dynamical Binary Neural Networks with Equilibrium Propagation
Jérémie Laydevant, Maxence Ernoult, Damien Querlioz +1
Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, thanks to the local updates of weights given by the internal dynamics of th…
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
Erwann Martin, Maxence Ernoult, Jérémie Laydevant +4
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.…
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier +3
Equilibrium Propagation (EP) is a biologically-inspired algorithm for convergent RNNs with a local learning rule that comes with strong theoretical guarantees. The parameter update…
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
Maxence Ernoult, Julie Grollier, Damien Querlioz +2
Equilibrium Propagation (EP) is a biologically inspired alternative algorithm to backpropagation (BP) for training neural networks. It applies to RNNs fed by a static input x that…