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 1501–1525 of 1963 papers

TitleStatusHype
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—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
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Collaborative Gaussian Processes for Preference Learning—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining Parametric Land Surface Models with Machine Learning—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Comparing noisy neural population dynamics using optimal transport distances—0
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Benchmark Results

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