SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 251–275 of 1963 papers

TitleStatusHype
Comparing noisy neural population dynamics using optimal transport distances—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Bayesian Kernel Shaping for Learning Control—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Bandits for Learning to Explain from Explanations—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Aligned Multi-Task Gaussian Process—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Algorithmic Linearly Constrained Gaussian Processes—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified