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

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Showing 726–750 of 1963 papers

TitleStatusHype
Discovering and forecasting extreme events via active learning in neural operators—0
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processesCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Autoencoder Attractors for Uncertainty EstimationCode0
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data—0
Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes—0
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—0
On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes—0
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel—0
Modelling variability in vibration-based PBSHM via a generalised population form—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression—0
Evaluating feasibility of batteries for second-life applications using machine learning—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Building 3D Generative Models from Minimal Data—0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty—0
Learning Invariant Weights in Neural Networks—0
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes—0
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Benchmark Results

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