Thermodynamic networks using non-equilibrium steady states achieve universal function approximation when engineered with negative differential conductance, as shown in quantum dot and enzymatic examples for sine fitting and MNIST classification.
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Introduces layer-wise learning signals combining knowledge distillation and local errors into Equilibrium Propagation, enabling scalable training of deep VGG-style CRNNs with SOTA results on CIFAR-10 and CIFAR-100.
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Thermodynamic Networks: Harnessing Non-Equilibrium Steady States for Computation
Thermodynamic networks using non-equilibrium steady states achieve universal function approximation when engineered with negative differential conductance, as shown in quantum dot and enzymatic examples for sine fitting and MNIST classification.
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Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs
Introduces layer-wise learning signals combining knowledge distillation and local errors into Equilibrium Propagation, enabling scalable training of deep VGG-style CRNNs with SOTA results on CIFAR-10 and CIFAR-100.