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 671–680 of 1963 papers

TitleStatusHype
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Functional Variational Bayesian Neural NetworksCode0
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite KernelCode0
Federated Causal Inference from Observational DataCode0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Graph Based Gaussian Processes on Restricted Domains—0
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Benchmark Results

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