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Found 5 papers, 4 papers with code
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Specifically, its training cost is independent of the number of training points.
Correcting Model Bias with Sparse Implicit Processes
Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure.
Deep Variational Implicit Processes
This generalization is similar to that of deep GPs over GPs, but it is more flexible due to the use of IPs as the prior distribution over the latent functions.
Function-space Inference with Sparse Implicit Processes
The result is a scalable method for approximate inference with IPs that can tune the prior IP parameters to the data, and that provides accurate non-Gaussian predictive distributions.
Adversarial $α$-divergence Minimization for Bayesian Approximate Inference
Estimating the uncertainty in the predictions is a critical aspect with important applications, and one method to obtain this information is following a Bayesian approach to estimate a posterior distribution on the model parameters.
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