Gradient Descent
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Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks Y uhang Li
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Spiking neural networks (SNNs) have emerged as a biology-inspired method mimicking the spiking nature of brain neurons.
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Meta-Learning to Improve Pre-Training
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Choosing optimal PT hyperparameter values is challenging, and existing methods do not work well.
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Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent
Neural Information Processing Systems
We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric.
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