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 1276–1300 of 1963 papers

TitleStatusHype
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
Combining Parametric Land Surface Models with Machine Learning—0
PACOH: Bayes-Optimal Meta-Learning with PAC-GuaranteesCode1
Graph Convolutional Gaussian Processes For Link Prediction—0
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural NetworksCode0
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Linearly Constrained Neural NetworksCode0
Linearly Constrained Gaussian Processes with Boundary Conditions—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Transport Gaussian Processes for Regression—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Multi-class Gaussian Process Classification with Noisy InputsCode1
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Scalable Hyperparameter Optimization with Lazy Gaussian ProcessesCode0
Quantified limits of the nuclear landscape—0
Doubly Sparse Variational Gaussian Processes—0
Considering discrepancy when calibrating a mechanistic electrophysiology modelCode0
Bayesian Quantile and Expectile Optimisation—0
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational AutoencodersCode1
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes—0
Inter-domain Deep Gaussian Processes with RKHS Fourier Features—0
Healing Gaussian Process Experts—0
Influenza Forecasting Framework based on Gaussian Processes—0
Show:102550
← PrevPage 52 of 79Next →

Benchmark Results

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