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 1276–1300 of 1963 papers

TitleStatusHype
Improved active output selection strategy for noisy environments—0
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Improving Random Forests by Smoothing—0
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes—0
Incremental Ensemble Gaussian Processes—0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Inducing Gaussian Process Networks—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Large Scale Regression Models with Dependent Errors—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Infinite attention: NNGP and NTK for deep attention networks—0
Infinite-channel deep stable convolutional neural networks—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Infinite Mixtures of Multivariate Gaussian Processes—0
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Benchmark Results

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