41 citations · 152 across the 18 of their papers we have counts for
11 papers · 1 filter
Training a Predictive Coding Network on ImageNet using Equilibrium Propagation
Tugdual Kerjan, Rasmus Høier, Benjamin Scellier
Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfield networks, nonlinear resist…
Energy-based learning algorithms for analog computing: a comparative study
Benjamin Scellier, Maxence Ernoult, Jack Kendall +1
Energy-based learning algorithms have recently gained a surge of interest due to their compatibility with analog (post-digital) hardware. Existing algorithms include contrastive le…
A universal approximation theorem for nonlinear resistive networks
Benjamin Scellier, Siddhartha Mishra
Resistor networks have recently been studied as analog computing platforms for machine learning, particularly due to their compatibility with the Equilibrium Propagation training f…
Agnostic Physics-Driven Deep Learning
Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio +1
This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combin…
A deep learning theory for neural networks grounded in physics
Benjamin Scellier
In the last decade, deep learning has become a major component of artificial intelligence. The workhorse of deep learning is the optimization of loss functions by stochastic gradie…
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 counterpart of Backpropagation Through Time (BPTT) which, owing to its strong theoretical guarantees and the locality in spa…