A Stein variational method that prunes and aligns an ensemble of neural networks during training, yielding sparse models with parameter-level uncertainty estimates.
Improv- ing the performance of stein variational inference through extreme sparsification of physically-constrained neural network models
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Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
A Stein variational method that prunes and aligns an ensemble of neural networks during training, yielding sparse models with parameter-level uncertainty estimates.