
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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Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Equilibrium Propagation (EP) is a biologicallyinspired algorithm for co...
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Training EndtoEnd Analog Neural Networks with Equilibrium Propagation
We introduce a principled method to train endtoend analog neural netwo...
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Equilibrium Propagation with Continual Weight Updates
Equilibrium Propagation (EP) is a learning algorithm that bridges Machin...
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Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
Equilibrium Propagation (EP) is a biologically inspired alternative algo...
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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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Generalization of Equilibrium Propagation to Vector Field Dynamics
The biological plausibility of the backpropagation algorithm has long be...
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Equivalence of Equilibrium Propagation and Recurrent Backpropagation
Recurrent Backpropagation and Equilibrium Propagation are algorithms for...
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Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
We consider deep multilayered generative models such as Boltzmann machi...
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Equilibrium Propagation: Bridging the Gap Between EnergyBased Models and Backpropagation
We introduce Equilibrium Propagation, a learning framework for energyba...
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Benjamin Scellier
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