Further, we emphasize the significance of principled regularization of the network parameters and prediction.

However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates.

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First, it requires efficient and flexible parameterisations of layer-wise equivari-ances.

Whilemuchwork has focused on different weight pruning criteria, the overallsparsifiabilityofthe network, i.e., its capacity to be pruned without quality loss, has often been overlooked.

Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets.