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 976–1000 of 1963 papers

TitleStatusHype
Adaptive Inducing Points Selection For Gaussian Processes—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
Subset-of-Data Variational Inference for Deep Gaussian-Processes RegressionCode0
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Input Dependent Sparse Gaussian Processes—0
Spectrum Gaussian Processes Based On Tunable Basis Functions—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction—0
Scaling Gaussian Processes with Derivative Information Using Variational Inference—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Random Neural Networks in the Infinite Width Limit as Gaussian Processes—0
Scale Mixtures of Neural Network Gaussian ProcessesCode0
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization—0
Evolving-Graph Gaussian ProcessesCode0
Variance Reduction for Matrix Computations with Applications to Gaussian Processes—0
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors—0
Probabilistic analysis of solar cell optical performance using Gaussian processes—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear TimeCode0
Deep Gaussian Processes: A Survey—0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
Leveraging Probabilistic Circuits for Nonparametric Multi-Output RegressionCode0
Last Layer Marginal Likelihood for Invariance LearningCode0
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Benchmark Results

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