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 851–875 of 1963 papers

TitleStatusHype
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian ProcessesCode0
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice—0
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learningCode0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
Deep banach space kernels—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
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
A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements—0
Approximate Latent Force Model InferenceCode0
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
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes—0
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
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
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Benchmark Results

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