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 1826–1850 of 1963 papers

TitleStatusHype
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes LearningCode0
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover TreesCode0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
The Currents of Conflict: Decomposing Conflict Trends with Gaussian ProcessesCode0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
On Bayesian Search for the Feasible Space Under Computationally Expensive ConstraintsCode0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Classification with Gaussian ProcessesCode0
Transfer Learning with Gaussian Processes for Bayesian OptimizationCode0
The Fast Kernel TransformCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian ManifoldsCode0
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian ProcessesCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
On Exact Computation with an Infinitely Wide Neural NetCode0
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
Unsupervised cell segmentation by fast Gaussian ProcessesCode0
Approximate Latent Force Model InferenceCode0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-timeCode0
Safe Exploration in Finite Markov Decision Processes with Gaussian ProcessesCode0
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Additive Gaussian Processes RevisitedCode0
Show:102550
← PrevPage 74 of 79Next →

Benchmark Results

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