SA-SGLD adapts SGLD stepsizes via gradient-norm-based time rescaling to sample BNN posteriors more accurately than standard SGLD on toy examples and image classification tasks without introducing bias.
4.(θ n, ζn)is ergodic Markov with invariant measureeπ h
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Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
SA-SGLD adapts SGLD stepsizes via gradient-norm-based time rescaling to sample BNN posteriors more accurately than standard SGLD on toy examples and image classification tasks without introducing bias.