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
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Analysis of Financial Credit Risk Using Machine Learning—0
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—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
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
Adaptive Inducing Points Selection For Gaussian Processes—0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Building 3D Generative Models from Minimal Data—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—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
BOIS: Bayesian Optimization of Interconnected Systems—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
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—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
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—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