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

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Showing 1776–1800 of 1963 papers

TitleStatusHype
Using Gaussian Processes for Rumour Stance Classification in Social Media—0
Scalable Hyperparameter Optimization with Products of Gaussian Process ExpertsCode0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
Bayesian Learning of Dynamic Multilayer Networks—0
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF SurrogatesCode0
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
Safe Exploration in Finite Markov Decision Processes with Gaussian ProcessesCode0
Understanding Probabilistic Sparse Gaussian Process Approximations—0
Prediction performance after learning in Gaussian process regression—0
Policy Networks with Two-Stage Training for Dialogue Systems—0
Gaussian Processes for Music Audio Modelling and Content Analysis—0
Differentially Private Gaussian Processes—0
What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution—0
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation PropagationCode0
Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding—0
Stochastic Portfolio Theory: A Machine Learning Perspective—0
Matching models across abstraction levels with Gaussian Processes—0
Deep Multi-fidelity Gaussian ProcessesCode0
Scalable Gaussian Processes for Supervised Hashing—0
Chained Gaussian ProcessesCode0
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis—0
Fast methods for training Gaussian processes on large data sets—0
Gaussian Process Morphable Models—0
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Benchmark Results

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