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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–900 of 1963 papers

TitleStatusHype
Aggregating Dependent Gaussian Experts in Local Approximation—0
Group Importance Sampling for Particle Filtering and MCMC—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Linear-time inference for Gaussian Processes on one dimension—0
Data Association with Gaussian Processes—0
Harmonizable mixture kernels with variational Fourier features—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
A Unified Kernel for Neural Network Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Gauss-Legendre Features for Gaussian Process Regression—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Gaussian Process Volatility Model—0
Current Methods for Drug Property Prediction in the Real World—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Correcting Model Bias with Sparse Implicit Processes—0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process on the Product of Directional Manifolds—0
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Benchmark Results

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