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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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Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

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A Bayesian Perspective on Training Speed and Model Selection

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Bayesian Image Classification with Deep Convolutional Gaussian Processes

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Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

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Variational Gaussian Process Models without Matrix Inverses

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Bayesian Layers: A Module for Neural Network Uncertainty

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