AIBullisharXiv – CS AI · 18h ago6/10
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Hybridizing Equilibrium Propagation with Ising Machines for Efficient Energy-Based Learning
Researchers propose a hybrid framework combining equilibrium propagation with Ising machine dynamics to improve energy-efficient neural network training. The approach replaces dissipative Hopfield relaxation with extended phase-space dynamics, achieving convergence speeds and accuracy comparable to backpropagation while reducing computational energy demands on deep convolutional networks.