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 1001–1050 of 1963 papers

TitleStatusHype
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks—0
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes—0
Towards Recurrent Autoregressive Flow Models—0
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks—0
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6—0
Trained quantum neural networks are Gaussian processes—0
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation—0
Transductive Kernels for Gaussian Processes on Graphs—0
Transductive Learning for Multi-Task Copula Processes—0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Transport Gaussian Processes for Regression—0
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions—0
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation—0
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes—0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
Twin gaussian processes for structured prediction—0
Two Gaussian Approaches to Black-Box Optomization—0
Fast Deep Mixtures of Gaussian Process Experts—0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
Uncertainty-aware Remaining Useful Life predictor—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process—0
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality—0
Understanding Probabilistic Sparse Gaussian Process Approximations—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control—0
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Universal low-rank matrix recovery from Pauli measurements—0
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN—0
Upgrading from Gaussian Processes to Student's-T Processes—0
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design—0
Using BART to Perform Pareto Optimization and Quantify its Uncertainties—0
Using Contextual Information to Improve Blood Glucose Prediction—0
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes—0
Using Distance Correlation for Efficient Bayesian Optimization—0
Using Gaussian Processes for Rumour Stance Classification in Social Media—0
Using scientific machine learning for experimental bifurcation analysis of dynamic systems—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Value-at-Risk Optimization with Gaussian Processes—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
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Benchmark Results

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