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 1626–1650 of 1963 papers

TitleStatusHype
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Emulating dynamic non-linear simulators using Gaussian processes—0
The Gaussian Process Autoregressive Regression Model (GPAR)Code1
Analysis of Financial Credit Risk Using Machine Learning—0
Prophit: Causal inverse classification for multiple continuously valued treatment policies—0
State Space Gaussian Processes with Non-Gaussian Likelihood—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Practical Transfer Learning for Bayesian OptimizationCode0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface—0
Scalable Lévy Process Priors for Spectral Kernel LearningCode0
Composite Gaussian Processes: Scalable Computation and Performance Analysis—0
Kernel Distillation for Fast Gaussian Processes Prediction—0
Probabilistic Recurrent State-Space ModelsCode1
Algorithmic Linearly Constrained Gaussian Processes—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Upgrading from Gaussian Processes to Student's-T Processes—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"Code0
Multiscale Sparse Microcanonical Models—0
PHOENICS: A universal deep Bayesian optimizerCode0
Intrinsic Gaussian processes on complex constrained domains—0
Gaussian Process Neurons—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
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Benchmark Results

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