50 citations · 100 across the 11 of their papers we have counts for
3 papers · 1 filter
How Deep Are Deep Gaussian Processes?
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart +1
Recent research has shown the potential utility of Deep Gaussian Processes. These deep structures are probability distributions, designed through hierarchical construction, which a…
Iterative Updating of Model Error for Bayesian Inversion
Daniela Calvetti, Matthew M. Dunlop, Erkki Somersalo +1
In computational inverse problems, it is common that a detailed and accurate forward model is approximated by a computationally less challenging substitute. The model reduction may…
Reconciling Bayesian and perimeter regularization for binary inversion
Oliver R. A. Dunbar, Matthew M. Dunlop, Charles M. Elliott +2
A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization via the use of the total variation. On…