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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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Correlated weights in infinite limits of deep convolutional neural networks

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Deep Neural Networks as Point Estimates for Deep Gaussian Processes

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Speedy Performance Estimation for Neural Architecture Search

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The promises and pitfalls of deep kernel learning

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