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 551–575 of 1963 papers

TitleStatusHype
Hodge-Compositional Edge Gaussian ProcessesCode0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Deep Transformed Gaussian Processes—0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regressionCode0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models—0
Infinite Width Graph Neural Networks for Node Regression/ ClassificationCode0
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies—0
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes—0
Stationarity without mean reversion in improper Gaussian processes—0
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints—0
Leave-one-out Distinguishability in Machine LearningCode0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points—0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processesCode0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
How to turn your camera into a perfect pinhole model—0
Show:102550
← PrevPage 23 of 79Next →

Benchmark Results

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