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Found 4 papers, 3 papers with code
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
To generalize Tucker decomposition to such scenarios, we propose Functional Bayesian Tucker Decomposition (FunBaT).
Multi-Resolution Active Learning of Fourier Neural Operators
To overcome this problem, we propose Multi-Resolution Active learning of FNO (MRA-FNO), which can dynamically select the input functions and resolutions to lower the data cost as much as possible while optimizing the learning efficiency.
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy.
Scalable Variational Gaussian Process Regression Networks
Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable.
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