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 951–1000 of 1963 papers

TitleStatusHype
DEBOSH: Deep Bayesian Shape Optimization—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Sparse Gaussian Processes for Stochastic Differential Equations—0
Approximate Latent Force Model InferenceCode0
A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes—0
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Mapping Input Noise to Escape Noise in Integrate-and-fire neurons: A Level-Crossing Approach—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical ApplicationsCode0
Global Convolutional Neural ProcessesCode0
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep—0
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Neural Network Gaussian Processes by Increasing DepthCode0
Approximate Bayesian Optimisation for Neural Networks—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots—0
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference—0
A brief note on understanding neural networks as Gaussian processes—0
Adaptive Inducing Points Selection For Gaussian Processes—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
Subset-of-Data Variational Inference for Deep Gaussian-Processes RegressionCode0
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Input Dependent Sparse Gaussian Processes—0
Spectrum Gaussian Processes Based On Tunable Basis Functions—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction—0
Scaling Gaussian Processes with Derivative Information Using Variational Inference—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Random Neural Networks in the Infinite Width Limit as Gaussian Processes—0
Scale Mixtures of Neural Network Gaussian ProcessesCode0
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization—0
Evolving-Graph Gaussian ProcessesCode0
Variance Reduction for Matrix Computations with Applications to Gaussian Processes—0
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors—0
Probabilistic analysis of solar cell optical performance using Gaussian processes—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear TimeCode0
Deep Gaussian Processes: A Survey—0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
Leveraging Probabilistic Circuits for Nonparametric Multi-Output RegressionCode0
Last Layer Marginal Likelihood for Invariance LearningCode0
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Benchmark Results

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