Introduces Ising-dynamics-inspired equilibrium propagation with extended phase-space dynamics to lower energy barriers and train deep convolutional Hopfield networks on MNIST, FashionMNIST, and CIFAR-10 at backpropagation-comparable accuracy.
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A VGG10 predictive coding network is trained on ImageNet via equilibrium propagation to 13.23% top-5 error, close to the 12.2% backpropagation baseline, marking the first such demonstration at this scale.
Hybrid optical implementation of equilibrium propagation via spatial photonic Ising machine demonstrated on Wine classification with numerical MNIST evaluation.
citing papers explorer
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Hybridizing Equilibrium Propagation with Ising Machines for Efficient Energy-Based Learning
Introduces Ising-dynamics-inspired equilibrium propagation with extended phase-space dynamics to lower energy barriers and train deep convolutional Hopfield networks on MNIST, FashionMNIST, and CIFAR-10 at backpropagation-comparable accuracy.
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Training a Predictive Coding Network on ImageNet using Equilibrium Propagation
A VGG10 predictive coding network is trained on ImageNet via equilibrium propagation to 13.23% top-5 error, close to the 12.2% backpropagation baseline, marking the first such demonstration at this scale.