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

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Showing 1126–1150 of 1963 papers

TitleStatusHype
Stein Variational Gaussian ProcessesCode0
An Intuitive Tutorial to Gaussian Process RegressionCode1
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers—0
Time series forecasting with Gaussian Processes needs priorsCode1
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children—0
Contraction L_1-Adaptive Control using Gaussian Processes—0
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion MethodCode0
Information Theoretic Meta Learning with Gaussian Processes—0
Modulating Scalable Gaussian Processes for Expressive Statistical LearningCode0
Locally induced Gaussian processes for large-scale simulation experiments—0
Fast Approximate Multi-output Gaussian ProcessesCode0
Neural Networks and Quantum Field TheoryCode1
Preferential Bayesian optimisation with Skew Gaussian Processes—0
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization—0
Continuous Optimization Benchmarks by SimulationCode0
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling—0
Deep State-Space Gaussian ProcessesCode1
Multi-Agent Safe Planning with Gaussian Processes—0
Deterministic error bounds for kernel-based learning techniques under bounded noiseCode0
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes—0
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression—0
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes—0
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Benchmark Results

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