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 1301–1325 of 1963 papers

TitleStatusHype
Combining Parametric Land Surface Models with Machine Learning—0
Graph Convolutional Gaussian Processes For Link Prediction—0
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
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Quantified limits of the nuclear landscape—0
Scalable Hyperparameter Optimization with Lazy Gaussian ProcessesCode0
Doubly Sparse Variational Gaussian Processes—0
Considering discrepancy when calibrating a mechanistic electrophysiology modelCode0
Bayesian Quantile and Expectile Optimisation—0
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes—0
Influenza Forecasting Framework based on Gaussian Processes—0
Inter-domain Deep Gaussian Processes with RKHS Fourier Features—0
Healing Gaussian Process Experts—0
Randomly Projected Additive Gaussian Processes for RegressionCode0
Disentangling Trainability and Generalization in Deep Neural Networks—0
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase—0
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesCode0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
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Benchmark Results

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