Gaussian Processes
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Benchmarks
UCI POWER
Most implemented
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Conditional Neural Processes
Gaussian Processes for Big Data
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Adversarial Robustness Toolbox v1.0.0
Deep Neural Networks as Gaussian Processes
Papers
Gaussian Processes for Modelling Spatial Fields with Robot Swarms
Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terrain elevation. However, existing methods …
Gaussian ProcessesGeneralized Splines and Gaussian Processes
For finite-dimensional linear inverse problems where the variables are Gaussian, it is well-known that the minimum-mean-square error estimator takes the form of a regularized least-squares data fit. In this chapter, we s…
Gaussian ProcessesCDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation
Industrial visual inspection must both decide whether a product is defective and localize the defect, yet pixel-level masks are costly to collect at scale. Most anomaly-segmentation methods learn only from defect-free im…
Gaussian ProcessesModified Bryson-Frazier Smoothing and Hyperparameter Learning for Temporal Gaussian Process Regression
One-dimensional Gaussian processes with stationary, integrable kernel functions admit exact or arbitrarily accurate state-space representations, enabling linear-time inference through Kalman filtering and Rauch-Tung-Stri…
Gaussian ProcessesOn the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via vario…
Hyperparameter OptimizationGaussian ProcessesConformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models
Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs. Ho…
Gaussian Processes