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 176–200 of 1963 papers

TitleStatusHype
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