Bayesian learning for neural networks

E1031257

Bayesian method machine learning method neural network training approach probabilistic modeling technique

Bayesian learning for neural networks is an approach that applies Bayesian inference to neural network models, treating their weights as probability distributions to improve uncertainty estimation and generalization.

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All labels observed (7)

Label	Occurrences
Bayesian neural networks	4
Bayesian Learning for Neural Networks	3
Weight Uncertainty in Neural Networks	2
Bayes by Backprop	1
Bayesian deep learning	1
Bayesian learning for neural networks canonical	1
Bayesian methods for neural networks	1

How this entity was disambiguated

This entity first appeared as the object of triple T13267017 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Bayesian learning for neural networks
Context triple: [Radford M. Neal, knownFor, Bayesian learning for neural networks]

A. Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
B. Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
C. Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
D. “A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
E. Cascade-Correlation learning architecture
Cascade-Correlation learning architecture is a neural network training method that incrementally builds its own topology by adding new hidden units during learning to improve performance.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Bayesian learning for neural networks
Target entity description: Bayesian learning for neural networks is an approach that applies Bayesian inference to neural network models, treating their weights as probability distributions to improve uncertainty estimation and generalization.

A. Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
B. Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
C. Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
D. “A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
E. Cascade-Correlation learning architecture
Cascade-Correlation learning architecture is a neural network training method that incrementally builds its own topology by adding new hidden units during learning to improve performance.
F. None of above. chosen

Statements (48)

Predicate	Object
instanceOf	Bayesian method ⓘ machine learning method ⓘ neural network training approach ⓘ probabilistic modeling technique ⓘ
aimsAt	improving generalization ⓘ improving uncertainty estimation ⓘ
appliesTo	neural networks ⓘ
assumes	likelihood model for data given weights ⓘ
benefits	out-of-distribution detection ⓘ small-data regimes ⓘ
canUse	dropout as approximate Bayesian inference ⓘ ensembles as approximate Bayesian methods ⓘ
computes	posterior over weights given data ⓘ
contrastsWith	empirical risk minimization with deterministic weights ⓘ maximum likelihood training of neural networks ⓘ point-estimate training of neural networks ⓘ
enables	better decision making under uncertainty ⓘ calibrated predictive probabilities ⓘ principled uncertainty quantification ⓘ
facesChallenge	computational complexity ⓘ intractable exact posteriors ⓘ
helpsWith	model selection ⓘ overfitting control ⓘ regularization of neural networks ⓘ
isUsedIn	Bayesian optimization ⓘ active learning ⓘ reinforcement learning ⓘ safety-critical applications ⓘ uncertainty-aware prediction ⓘ
models	parameter uncertainty ⓘ predictive uncertainty ⓘ
oftenUses	Bayesian model averaging ⓘ Laplace approximation ⓘ linked to: Laplace method Markov chain Monte Carlo ⓘ Monte Carlo sampling ⓘ expectation propagation ⓘ variational inference ⓘ
produces	posterior predictive distribution ⓘ
relatedTo	Bayesian neural networks ⓘ linked to: Bayesian learning for neural networks Gaussian process approximations ⓘ probabilistic deep learning ⓘ
represents	weights with probability distributions ⓘ
requires	approximate inference methods ⓘ
treats	network weights as random variables ⓘ
uses	Bayesian inference ⓘ
usesConcept	Bayes theorem ⓘ linked to: Bayes’ theorem posterior distribution over weights ⓘ prior distribution over weights ⓘ

How these facts were elicited