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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 121–130 of 1963 papers

TitleStatusHype
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Convolutional conditional neural processes for local climate downscalingCode1
Near-linear Time Gaussian Process Optimization with Adaptive Batching and ResparsificationCode1
Neural-BO: A Black-box Optimization Algorithm using Deep Neural NetworksCode1
Neural Networks and Quantum Field TheoryCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
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Benchmark Results

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