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Found 6 papers, 1 papers with code
Gradient-enhanced deep Gaussian processes for multifidelity modelling
Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process.
A PAC-Bayesian Perspective on the Interpolating Information Criterion
Deep learning is renowned for its theory-practice gap, whereby principled theory typically fails to provide much beneficial guidance for implementation in practice.
The Interpolating Information Criterion for Overparameterized Models
The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset.
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
One prominent issue is the curse of dimensionality: it is commonly believed that the marginal likelihood should be reminiscent of cross-validation metrics and that both should deteriorate with larger input dimensions.
Stochastic Normalizing Flows
We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs).
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Secondly, and more generally, we analyse the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions.
