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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 976–1000 of 1963 papers

TitleStatusHype
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization—0
The Gaussian Process Latent Autoregressive Model—0
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning—0
The Human Kernel—0
The Limitations of Model Uncertainty in Adversarial Settings—0
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain—0
The Multivariate Generalised von Mises distribution: Inference and applications—0
Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions—0
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation—0
The Promises and Pitfalls of Deep Kernel Learning—0
The Random Forest Kernel and other kernels for big data from random partitions—0
The Recycling Gibbs Sampler for Efficient Learning—0
The role of a layer in deep neural networks: a Gaussian Process perspective—0
The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface—0
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning—0
The Use of Gaussian Processes in System Identification—0
The Impact of Data on the Stability of Learning-Based Control- Extended Version—0
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes—0
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory—0
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients—0
Tighter sparse variational Gaussian processes—0
Time-changed normalizing flows for accurate SDE modeling—0
Time Series Counterfactual Inference with Hidden Confounders—0
Time-Varying Transition Matrices with Multi-task Gaussian Processes—0
TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex imaging—0
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Benchmark Results

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