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 1781–1790 of 1963 papers

TitleStatusHype
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
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Benchmark Results

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