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 1601–1625 of 1963 papers

TitleStatusHype
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Deep Horseshoe Gaussian Processes—0
Deep Importance Sampling based on Regression for Model Inversion and Emulation—0
Deep kernel processes—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Deep Manifold Prior—0
Meta-Learning Mean Functions for Gaussian Processes—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Quantum neural networks form Gaussian processes—0
Deep Random Splines for Point Process Intensity Estimation—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Sigma Point Processes—0
Deep Transformed Gaussian Processes—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Dependence between Bayesian neural network units—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Design of Experiments for Verifying Biomolecular Networks—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization—0
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Benchmark Results

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