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 151–200 of 1963 papers

TitleStatusHype
Semi-Supervised Image Deraining using Gaussian ProcessesCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Neural Networks and Quantum Field TheoryCode1
Deep State-Space Gaussian ProcessesCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Random Forests for dependent dataCode1
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciencesCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian ProcessesCode1
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
Matérn Gaussian processes on Riemannian manifoldsCode1
70 years of machine learning in geoscience in reviewCode1
Syn2Real Transfer Learning for Image Deraining using Gaussian ProcessesCode1
Variational Auto-Regressive Gaussian Processes for Continual LearningCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Quadruply Stochastic Gaussian ProcessesCode1
Skew Gaussian Processes for ClassificationCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processesCode1
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change ScenariosCode1
Near-linear Time Gaussian Process Optimization with Adaptive Batching and ResparsificationCode1
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
PACOH: Bayes-Optimal Meta-Learning with PAC-GuaranteesCode1
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational AutoencodersCode1
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian ProcessesCode1
Scalable Exact Inference in Multi-Output Gaussian ProcessesCode1
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian ProcessesCode1
Sparse Orthogonal Variational Inference for Gaussian ProcessesCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Gaussian Process Optimization with Adaptive Sketching: Scalable and No RegretCode1
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)Code1
Conditional Neural ProcessesCode1
Neural Tangent Kernel: Convergence and Generalization in Neural NetworksCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for HealthcareCode1
The Gaussian Process Autoregressive Regression Model (GPAR)Code1
Probabilistic Recurrent State-Space ModelsCode1
Deep Kernel LearningCode1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)Code1
Fast Gaussian Processes under Monotonicity Constraints—0
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Benchmark Results

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