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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 1451–1500 of 1963 papers

TitleStatusHype
Functional Variational Bayesian Neural NetworksCode0
Gaussian Process Optimization with Adaptive Sketching: Scalable and No RegretCode1
Learning Gaussian Policies from Corrective Human Feedback—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test EvaluationsCode0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
Bayesian Anomaly Detection and Classification—0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent—0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation—0
Low-pass filtering as Bayesian inference—0
The role of a layer in deep neural networks: a Gaussian Process perspective—0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach—0
Ensembling methods for countrywide short term forecasting of gas demand—0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Towards Practical Lipschitz Bandits—0
On the Limitations of Representing Functions on Sets—0
Meta-Learning Mean Functions for Gaussian Processes—0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Gaussian processes with linear operator inequality constraintsCode0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Performance prediction of data streams on high-performance architecture—0
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler—0
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Scalable GAM using sparse variational Gaussian processes—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes—0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
Physics-Based Learning for Robotic Environmental Sensing—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
The Limitations of Model Uncertainty in Adversarial Settings—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
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Benchmark Results

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