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Everything about Neural network Gaussian process
Distribution over functions corresponding to an infinitely wide Bayesian neural network
A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically, a wide variety of network architectures converges to a GP in the infinitely wide limit, in the sense of distribution. The concept constitutes an intensional definition, i.e., a NNGP is just a GP, but distinguished by how it is obtained. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Neural network Gaussian processA 5-minute orientation
- Skim a recent paper: Real-time energy performance benchmarking of electric vehicle air conditioning systems using adaptive neural network and Gaussian process regressionInfluential work since 2022
- Pick up a classic book: Independent component analysisThe most-reprinted book on the subject
- Read the landmark paper: Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian ProcessCited 138 times
- Go sideways: Cramér–Rao boundA closely related topic
- Go sideways: Ornstein–Uhlenbeck processA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process
Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression
Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression
Ultrasonic characterization of thermal barrier coatings porosity through BP neural network optimizing Gaussian process regression algorithm
Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression
Bayesian Optimization Approach for RF Circuit Synthesis via Multitask Neural Network Enhanced Gaussian Process
🆕 Influential since 2022
Real-time energy performance benchmarking of electric vehicle air conditioning systems using adaptive neural network and Gaussian process regression
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structure Assembly
Gaussian process regression based on deep neural network for reliability analysis in high dimensions
Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process
A Novel Neural Network With Gaussian Process Feedback for Modeling the State-of-Charge of Battery Cells
đź“š Books worth reading
Independent component analysisTe-Won Lee · 1998
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural DecodingMichael Craig Burkhart · 2019
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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