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 1626–1650 of 1963 papers

TitleStatusHype
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Product Kernel Interpolation for Scalable Gaussian Processes—0
Machine learning based hyperspectral image analysis: A survey—0
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector CalculusCode0
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)Code0
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision MakingCode0
VBALD - Variational Bayesian Approximation of Log Determinants—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Emulating dynamic non-linear simulators using Gaussian processes—0
Learning Integral Representations of Gaussian ProcessesCode0
Prophit: Causal inverse classification for multiple continuously valued treatment policies—0
Analysis of Financial Credit Risk Using Machine Learning—0
State Space Gaussian Processes with Non-Gaussian Likelihood—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Practical Transfer Learning for Bayesian OptimizationCode0
The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
Scalable Lévy Process Priors for Spectral Kernel LearningCode0
Kernel Distillation for Fast Gaussian Processes Prediction—0
Composite Gaussian Processes: Scalable Computation and Performance Analysis—0
Algorithmic Linearly Constrained Gaussian Processes—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Upgrading from Gaussian Processes to Student's-T Processes—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"Code0
Show:102550
← PrevPage 66 of 79Next →

Benchmark Results

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