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 741–750 of 1963 papers

TitleStatusHype
Evaluating feasibility of batteries for second-life applications using machine learning—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Building 3D Generative Models from Minimal Data—0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty—0
Learning Invariant Weights in Neural Networks—0
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes—0
Show:102550
← PrevPage 75 of 197Next →

Benchmark Results

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