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Learning Layer-wise Equivariances Automatically using Gradients

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Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

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Bayesian Neural Network Priors Revisited

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Data augmentation in Bayesian neural networks and the cold posterior effect

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Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

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Last Layer Marginal Likelihood for Invariance Learning

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Learning invariant weights in neural networks

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Memory Safe Computations with XLA Compiler

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Relaxing Equivariance Constraints with Non-stationary Continuous Filters

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SnAKe: Bayesian Optimization with Pathwise Exploration

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