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 201–250 of 1963 papers

TitleStatusHype
Analytical Results for the Error in Filtering of Gaussian Processes—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
Analysis of Financial Credit Risk Using Machine Learning—0
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—0
Building 3D Generative Models from Minimal Data—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Analogical-based Bayesian Optimization—0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
Adaptive Inducing Points Selection For Gaussian Processes—0
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Amortized Variational Inference for Deep Gaussian Processes—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian active learning for choice models with deep Gaussian processes—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Adaptive finite element type decomposition of Gaussian processes—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Baryons from Mesons: A Machine Learning Perspective—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—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
Amortized variance reduction for doubly stochastic objectives—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
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
BOIS: Bayesian Optimization of Interconnected Systems—0
A chain rule for the expected suprema of Gaussian processes—0
Bayesian estimation of orientation preference maps—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—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
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
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Benchmark Results

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