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 651–700 of 1963 papers

TitleStatusHype
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Visual Pursuit Control based on Gaussian Processes with Switched Motion TrajectoriesCode0
Quantum Bayesian Computation—0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
Gaussian Process Surrogate Models for Neural Networks—0
Bayesian Optimization with Informative Covariance—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Correcting Model Bias with Sparse Implicit Processes—0
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings—0
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—0
Low-Precision Arithmetic for Fast Gaussian ProcessesCode1
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian ProcessesCode1
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Off-the-grid learning of mixtures from a continuous dictionary—0
On the Rényi Cross-Entropy—0
Supernova Light Curves Approximation based on Neural Network ModelsCode1
Distributional Gaussian Processes Layers for Out-of-Distribution Detection—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)—0
Additive Gaussian Processes RevisitedCode0
LIMO: Latent Inceptionism for Targeted Molecule GenerationCode1
Shallow and Deep Nonparametric Convolutions for Gaussian ProcessesCode0
On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring—0
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian ProcessesCode0
Deep Variational Implicit ProcessesCode0
Multi-fidelity Hierarchical Neural ProcessesCode0
Neural Diffusion ProcessesCode1
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
Active Bayesian Causal InferenceCode1
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
Posterior and Computational Uncertainty in Gaussian ProcessesCode1
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
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data—0
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoodsCode0
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Benchmark Results

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