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Found 3 papers, 1 papers with code
Energy-based learning algorithms for analog computing: a comparative study
Additionally, we establish new SOTA results with DCHNs on all five datasets, both in performance and speed.
A Gradient Estimator for Time-Varying Electrical Networks with Non-Linear Dissipation
We propose a method for extending the technique of equilibrium propagation for estimating gradients in fixed-point neural networks to the more general setting of directed, time-varying neural networks by modeling them as electrical circuits.
Training End-to-End Analog Neural Networks with Equilibrium Propagation
We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent.
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