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Found 2 papers, 2 papers with code
Improving predictions of Bayesian neural nets via local linearization
The generalized Gauss-Newton (GGN) approximation is often used to make practical Bayesian deep learning approaches scalable by replacing a second order derivative with a product of first order derivatives.
Approximate Inference Turns Deep Networks into Gaussian Processes
Deep neural networks (DNN) and Gaussian processes (GP) are two powerful models with several theoretical connections relating them, but the relationship between their training methods is not well understood.
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