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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
Efficient Global Optimization using Deep Gaussian Processes—0
Group Importance Sampling for Particle Filtering and MCMC—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Harmonizable mixture kernels with variational Fourier features—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Healing Gaussian Process Experts—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Heavy-Tailed Process Priors for Selective Shrinkage—0
Deep banach space kernels—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Deep Bayesian Convolutional Networks with Many Channels are 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
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Aligned Multi-Task Gaussian Process—0
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
Infinite attention: NNGP and NTK for deep attention networks—0
Infinite-channel deep stable convolutional neural networks—0
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Deep Ensemble Kernel Learning—0
Mapping Leaf Area Index with a Smartphone and Gaussian Processes—0
How to turn your camera into a perfect pinhole model—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
Deep Factors with Gaussian Processes for Forecasting—0
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Efficient Determination of Safety Requirements for Perception Systems—0
Deep Gaussian Covariance Network—0
Matching models across abstraction levels with Gaussian Processes—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
Identifying Causal Direction via Variational Bayesian Compression—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
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Benchmark Results

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