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 281–290 of 1963 papers

TitleStatusHype
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
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
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Benchmark Results

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