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Found 4 papers, 1 papers with code
Flexible infinite-width graph convolutional networks and the importance of representation learning
A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed.
Convolutional Deep Kernel Machines
Recent work (A theory of representation learning gives a deep generalisation of kernel methods, Yang et al. 2023) modified the Neural Network Gaussian Process (NNGP) limit of Bayesian neural networks so that representation learning is retained.
An Improved Variational Approximate Posterior for the Deep Wishart Process
When the distribution is chosen to be Wishart, the model is called a deep Wishart process (DWP).
A theory of representation learning gives a deep generalisation of kernel methods
In particular, we show that Deep Gaussian processes (DGPs) in the Bayesian representation learning limit have exactly multivariate Gaussian posteriors, and the posterior covariances can be obtained by optimizing an interpretable objective combining a log-likelihood to improve performance with a series of KL-divergences which keep the posteriors close to the prior.
