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Found 3 papers, 2 papers with code
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
We compare trajectory sampling with density-based approximation for uncertainty propagation using three different probabilistic world models; Gaussian processes, Bayesian neural networks, and DGCNs.
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit
Therefore, we compared our proposed strategy to 9 adaptive sampling strategies for global surrogate modeling, based on 26 different 1 to 8-dimensional deterministic benchmarks functions.
Deep Gaussian Covariance Network
A possible solution to this, is the usage of a non-stationary covariance function, where the hyperparameters are calculated by a deep neural network.
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