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Non-asymptotic approximations of neural networks by Gaussian processes
The dataset is not explicitly described in the paper, but it is mentioned that the authors study the extent to which wide neural networks may be approximated by Gaussian processes. -
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Gaussian processes (GPs) are non-parametric, flexible models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially... -
Gaussian Processes on Graphs via Spectral Kernel Learning
Gaussian Processes on Graphs via Spectral Kernel Learning -
Gaussian Control Barrier Function
The dataset used in this paper is a set of safety samples or observations to construct the Gaussian Control Barrier Function (GCBF) online.
