-
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. -
Database Generation
A dataset of 25,000 (A(ω), G(τ )) pairs generated by first generating A and then computing from it G(τ ). Each A(ω) in the database is represented as a sum of R Gaussians with... -
Latent Variable Confounding Simulation Dataset
The dataset used in the paper is a latent variable confounding simulation dataset. -
Total Random Simulation Dataset
The dataset used in the paper is a total random simulation dataset. -
Regression Discontinuity Design Dataset
The dataset used in the paper is a regression discontinuity design dataset. -
Simulated Financial Data
Simulated financial data with an underlying structure, including a mean-reverting time series with an Ornstein-Uhlenbeck process. -
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 -
AFVSGP-HOCBF
The dataset used in the paper for real-time adaptive safety-critical control with Gaussian processes in high-order uncertain models. -
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.
