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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 701–750 of 1963 papers

TitleStatusHype
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
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
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
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
Modelling stellar activity with Gaussian process regression networksCode0
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problemsCode0
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep LearningCode0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
On boundary conditions parametrized by analytic functions—0
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Bézier Curve Gaussian Processes—0
Probabilistic Models for Manufacturing Lead Times—0
Know Thy Student: Interactive Learning with Gaussian Processes—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN—0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Inducing Gaussian Process Networks—0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Gaussian Processes for Missing Value ImputationCode1
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
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