How Deep Are Deep Gaussian Processes?
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Publication:4558207
zbMath1469.60107arXiv1711.11280MaRDI QIDQ4558207
A. L. Teckentrup, Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart
Publication date: 21 November 2018
Full work available at URL: https://arxiv.org/abs/1711.11280
Gaussian processes (60G15) Artificial neural networks and deep learning (68T07) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)
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Cites Work
- Unnamed Item
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- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- A Bayesian level set method for geometric inverse problems
- A general framework for the parametrization of hierarchical models
- Whittle-Matérn priors for Bayesian statistical inversion with applications in electrical impedance tomography
- Interpolation of spatial data. Some theory for kriging
- Statistical and computational inverse problems.
- Mitigating the influence of the boundary on PDE-based covariance operators
- Bayesian learning for neural networks
- Analysis of SPDEs arising in path sampling. I: The Gaussian case
- Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise.
- Bayesian Calibration of Computer Models
- Kullback--Leibler Approximation for Probability Measures on Infinite Dimensional Spaces
- Markov Chains and Stochastic Stability
- Iterated Random Functions
- Parameterizations for ensemble Kalman inversion
- Stable architectures for deep neural networks
- Combining Field Data and Computer Simulations for Calibration and Prediction
- Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
- Scattered Data Approximation
- MCMC methods for functions: modifying old algorithms to make them faster
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