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05/30/2022
Agnostic Physics-Driven Deep Learning
This work establishes that a physical system can perform statistical lea...
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03/18/2021
A deep learning theory for neural networks grounded in physics
In the last decade, deep learning has become a major component of artifi...
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06/06/2020
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Equilibrium Propagation (EP) is a biologically-inspired algorithm for co...
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06/02/2020
Training End-to-End Analog Neural Networks with Equilibrium Propagation
We introduce a principled method to train end-to-end analog neural netwo...
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04/29/2020
Equilibrium Propagation with Continual Weight Updates
Equilibrium Propagation (EP) is a learning algorithm that bridges Machin...
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04/29/2020
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
Equilibrium Propagation (EP) is a biologically inspired alternative algo...
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05/31/2019
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
Equilibrium Propagation (EP) is a biologically inspired learning algorit...
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08/14/2018
Generalization of Equilibrium Propagation to Vector Field Dynamics
The biological plausibility of the backpropagation algorithm has long be...
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11/22/2017
Equivalence of Equilibrium Propagation and Recurrent Backpropagation
Recurrent Backpropagation and Equilibrium Propagation are algorithms for...
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06/06/2016
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
We consider deep multi-layered generative models such as Boltzmann machi...
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02/16/2016
