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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1401–1450 of 1963 papers

TitleStatusHype
Modulating Surrogates for Bayesian Optimization—0
Modeling Severe Traffic Accidents With Spatial And Temporal Features—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
Sequential Neural ProcessesCode0
Compositionally-Warped Gaussian Processes—0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Black-Box Inference for Non-Linear Latent Force Models—0
Multi-resolution Multi-task Gaussian ProcessesCode0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
Learning Directed Graphical Models from Gaussian Data—0
Recurrent Neural ProcessesCode0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Kernelized Capsule NetworksCode0
Deep Compositional Spatial Models—0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Reliable training and estimation of variance networksCode0
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves—0
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Streaming Variational Monte CarloCode0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Bayesian Deconditional Kernel Mean Embeddings—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Neural Likelihoods for Multi-Output Gaussian Processes—0
Deep Bayesian Optimization on Attributed GraphsCode0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Monotonic Gaussian Process FlowCode0
Non-linear Multitask Learning with Deep Gaussian Processes—0
Adversarial Robustness Guarantees for Classification with Gaussian ProcessesCode0
Recursive Estimation for Sparse Gaussian Process RegressionCode0
Kernel Conditional Density Operators—0
Interpretable deep Gaussian processes with moments—0
Sequential Gaussian Processes for Online Learning of Nonstationary FunctionsCode0
A Bulirsch-Stoer algorithm using Gaussian processes—0
Learning spectrograms with convolutional spectral kernels—0
Efficient Deep Gaussian Process Models for Variable-Sized InputCode0
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes—0
Distribution Calibration for Regression—0
Deep Neural Architecture Search with Deep Graph Bayesian OptimizationCode0
Online Anomaly Detection with Sparse Gaussian Processes—0
Deep Gaussian Processes with Importance-Weighted Variational InferenceCode0
Graph Convolutional Gaussian Processes—0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
Bayesian Optimization using Deep Gaussian Processes—0
Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
On Exact Computation with an Infinitely Wide Neural NetCode0
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series—0
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Benchmark Results

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