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 801–850 of 1963 papers

TitleStatusHype
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
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Learning-based attacks in cyber-physical systems—0
A Hybrid Approach for Trajectory Control Design—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
Learning particle swarming models from data with Gaussian processes—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Global Optimization with Parametric Function Approximation—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
Global Optimization of Gaussian processes—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Gene Regulatory Network Inference with Latent Force Models—0
GP Kernels for Cross-Spectrum Analysis—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
Data-Driven Approaches for Modelling Target Behaviour—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Linear-time inference for Gaussian Processes on one dimension—0
Data Association with Gaussian Processes—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Graph Convolutional Gaussian Processes For Link Prediction—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Graphical LASSO Based Model Selection for Time Series—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Show:102550
← PrevPage 17 of 40Next →

Benchmark Results

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