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 1701–1725 of 1963 papers

TitleStatusHype
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Dual Parameterization of Sparse Variational Gaussian ProcessesCode0
Autoencoder Attractors for Uncertainty EstimationCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
GPEX, A Framework For Interpreting Artificial Neural NetworksCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
GPflowOpt: A Bayesian Optimization Library using TensorFlowCode0
Domain Invariant Learning for Gaussian Processes and Bayesian ExplorationCode0
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
Recovering BanditsCode0
Counterfactual Learning with Multioutput Deep KernelsCode0
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated DataCode0
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal DynamicsCode0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation PropagationCode0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU AccelerationCode0
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoodsCode0
Recurrent Gaussian ProcessesCode0
Gradients of Functions of Large MatricesCode0
Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural NetworksCode0
Recurrent Neural ProcessesCode0
Convolutional Deep Kernel MachinesCode0
Continuous Optimization Benchmarks by SimulationCode0
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Benchmark Results

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