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Bayesian neural networks with interpretable priors from Mercer kernels
Alex Alberts, Ilias Bilionis
Quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or no…
stat.ML2025
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation
Alex Alberts, Ilias Bilionis
Many inverse problems require reconstructing physical fields from limited and noisy data while incorporating known governing equations. A growing body of work within probabilistic…