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 191–200 of 1963 papers

TitleStatusHype
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?Code0
Artificial Neural Network and Deep Learning: Fundamentals and Theory—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian ProcessesCode0
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
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Benchmark Results

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