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 176–200 of 1963 papers

TitleStatusHype
Convolutional conditional neural processes for local climate downscalingCode1
Neural Networks and Quantum Field TheoryCode1
Gaussian processes at the Helm(holtz): A more fluid model for ocean currentsCode1
Nonnegative spatial factorizationCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Operator Learning with Gaussian ProcessesCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEsCode1
Physics Inspired Approaches To Understanding Gaussian ProcessesCode1
Posterior and Computational Uncertainty in Gaussian ProcessesCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive ControlCode1
Probabilistic selection of inducing points in sparse Gaussian processesCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
SEAL: Simultaneous Exploration and Localization in Multi-Robot SystemsCode1
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian ProcessesCode1
Deep Pipeline Embeddings for AutoMLCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Deep State-Space Gaussian ProcessesCode1
The Debiased Spatial Whittle LikelihoodCode0
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Benchmark Results

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