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 276–300 of 1963 papers

TitleStatusHype
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Auto-Differentiating Linear Algebra—0
Learning-based attacks in cyber-physical systems—0
A Hybrid Approach for Trajectory Control Design—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
A Unified Kernel for Neural Network Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Compositionally-Warped Gaussian Processes—0
Computational Graph Completion—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Attentive Gaussian processes for probabilistic time-series generation—0
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Benchmark Results

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