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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 701–725 of 1963 papers

TitleStatusHype
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibilityCode0
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios—0
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite KernelCode0
Modelling stellar activity with Gaussian process regression networksCode0
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problemsCode0
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep LearningCode0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
On boundary conditions parametrized by analytic functions—0
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Bézier Curve Gaussian Processes—0
Probabilistic Models for Manufacturing Lead Times—0
Know Thy Student: Interactive Learning with Gaussian Processes—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN—0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Inducing Gaussian Process Networks—0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Gaussian Processes for Missing Value ImputationCode1
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
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Benchmark Results

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