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 401–450 of 1963 papers

TitleStatusHype
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
Data Association with Gaussian Processes—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Data-Driven Approaches for Modelling Target Behaviour—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Complex-Valued Gaussian Processes for Regression—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
Comparing noisy neural population dynamics using optimal transport distances—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
25 Tweets to Know You: A New Model to Predict Personality with Social Media—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Data Fusion with Latent Map Gaussian Processes—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
DEBOSH: Deep Bayesian Shape Optimization—0
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
A brief note on understanding neural networks as Gaussian processes—0
Deep banach space kernels—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Aligned Multi-Task Gaussian Process—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Deep Compositional Spatial Models—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Deep Ensemble Kernel Learning—0
Bandits for Learning to Explain from Explanations—0
Deep Factors with Gaussian Processes for Forecasting—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Deep Gaussian Covariance Network—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
Design of Experiments for Verifying Biomolecular Networks—0
Deep Gaussian Processes: A Survey—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
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Benchmark Results

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