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 801–825 of 1963 papers

TitleStatusHype
Deep Variational Implicit ProcessesCode0
Multi-fidelity Hierarchical Neural ProcessesCode0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Statistical Deep Learning for Spatial and Spatio-Temporal Data—0
Constraining Gaussian processes for physics-informed acoustic emission mapping—0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing—0
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling—0
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates—0
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks—0
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoodsCode0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibilityCode0
An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios—0
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite KernelCode0
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problemsCode0
Modelling stellar activity with Gaussian process regression networksCode0
Show:102550
← PrevPage 33 of 79Next →

Benchmark Results

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