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 826–850 of 1963 papers

TitleStatusHype
Dream to Explore: Adaptive Simulations for Autonomous Systems—0
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Modular Gaussian Processes for Transfer LearningCode1
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control TasksCode0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
Variational Gaussian Processes: A Functional Analysis View—0
Using scientific machine learning for experimental bifurcation analysis of dynamic systems—0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
Computational Graph Completion—0
PriorVAE: Encoding spatial priors with VAEs for small-area estimationCode1
Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning—0
On Estimating the Probabilistic Region of Attraction for Partially Unknown Nonlinear Systems: An Sum-of-Squares Approach—0
Adversarial Attacks on Gaussian Process BanditsCode0
Function-space Inference with Sparse Implicit ProcessesCode0
Inferring Manifolds From Noisy Data Using Gaussian ProcessesCode0
Incremental Ensemble Gaussian Processes—0
On out-of-distribution detection with Bayesian neural networksCode0
Nonnegative spatial factorizationCode1
Learning to Pick at Non-Zero-Velocity from Interactive DemonstrationsCode1
LazyPPL: laziness and types in non-parametric probabilistic programs—0
Dense Gaussian Processes for Few-Shot SegmentationCode1
Gaussian Process for Trajectories—0
Probabilistic Metamodels for an Efficient Characterization of Complex Driving ScenariosCode0
Bayesian neural network unit priors and generalized Weibull-tail property—0
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian ProcessesCode0
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Benchmark Results

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