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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 201–210 of 1963 papers

TitleStatusHype
Scalable Machine Learning Algorithms using Path Signatures—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated DataCode0
The Currents of Conflict: Decomposing Conflict Trends with Gaussian ProcessesCode0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs—0
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Benchmark Results

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