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 1451–1475 of 1963 papers

TitleStatusHype
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Bayesian Anomaly Detection and Classification—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
Bayesian estimation of orientation preference maps—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Bayesian Inference of Log Determinants—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Bayesian Kernel Shaping for Learning Control—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
Bayesian Learning of Dynamic Multilayer Networks—0
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes—0
Bayesian Model Adaptation for Crowd Counts—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Bayesian Multi-Scale Optimistic Optimization—0
Bayesian neural network unit priors and generalized Weibull-tail property—0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control—0
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Benchmark Results

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