AIBullisharXiv – CS AI · May 117/10
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Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
Researchers propose a novel parameter reconstruction algorithm for training Spiking Neural Networks (SNNs) that addresses the long-standing problem of non-differentiable spike functions. The method extends convexification theory to recurrent networks and demonstrates consistent improvements over traditional surrogate gradient approaches, with potential applications in large-scale energy-efficient neural network training.