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 861–870 of 1963 papers

TitleStatusHype
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
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Benchmark Results

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