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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1250 of 1963 papers

TitleStatusHype
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks—0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Orthogonally Decoupled Variational Fourier Features—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications—0
Doubly infinite residual neural networks: a diffusion process approach—0
A Perspective on Gaussian Processes for Earth Observation—0
Motor cortex mapping using active gaussian processes—0
Sparse Gaussian Processes with Spherical Harmonic Features—0
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities—0
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes—0
Is SGD a Bayesian sampler? Well, almost—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Infinite attention: NNGP and NTK for deep attention networks—0
Likelihood-Free Inference with Deep Gaussian ProcessesCode0
Towards Recurrent Autoregressive Flow Models—0
Real-Time Regression with Dividing Local Gaussian Processes—0
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Uncertainty quantification using martingales for misspecified Gaussian processesCode0
Fast Deep Mixtures of Gaussian Process Experts—0
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions—0
Multi-Fidelity High-Order Gaussian Processes for Physical SimulationCode0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
All your loss are belong to BayesCode0
Physics Informed Deep Kernel Learning—0
Learning supported Model Predictive Control for Tracking of Periodic References—0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Regret Bound for Safe Gaussian Process Bandit Optimization—0
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning Inconsistent Preferences with Gaussian Processes—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels—0
A conditional one-output likelihood formulation for multitask Gaussian processesCode0
Non-Euclidean Universal ApproximationCode0
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression EstimatorsCode0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—0
Longitudinal Deep Kernel Gaussian Process Regression—0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Global Optimization of Gaussian processes—0
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
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Benchmark Results

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