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 751–800 of 1963 papers

TitleStatusHype
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Deep Gaussian Processes: A Survey—0
Deep Gaussian Processes—0
Baryons from Mesons: A Machine Learning Perspective—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
Adaptive finite element type decomposition of Gaussian processes—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Deep Gaussian Covariance Network—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Deep Factors with Gaussian Processes for Forecasting—0
Bandits for Learning to Explain from Explanations—0
Deep Ensemble Kernel Learning—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
Deep Compositional Spatial Models—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Aligned Multi-Task Gaussian Process—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Deep banach space kernels—0
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Algorithmic Linearly Constrained Gaussian Processes—0
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
DEBOSH: Deep Bayesian Shape Optimization—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
Data Fusion with Latent Map Gaussian Processes—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
25 Tweets to Know You: A New Model to Predict Personality with Social Media—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Auto-Differentiating Linear Algebra—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
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Benchmark Results

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