How to Train Your Resistive Network: Generalized Equilibrium Propagation and Analytical Learning
The paper addresses training analog computing systems by developing an algorithm to exactly calculate gradients using Kirchhoff's laws and graph theory. It introduces Generalized Equilibrium Propagation (GEP), which unifies learning rules like Equilibrium Propagation and Coupled Learning, allowing for the training of resistor networks without needing a full replica or readout over all components.