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.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Bayesian Learning for Neural Networks | 3 |
| 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
NERFINISHED
ⓘ
active learning ⓘ reinforcement learning ⓘ safety-critical applications ⓘ uncertainty-aware prediction ⓘ |
| models |
parameter uncertainty
ⓘ
predictive uncertainty ⓘ |
| oftenUses |
Bayesian model averaging
NERFINISHED
ⓘ
Laplace approximation NERFINISHED ⓘ Markov chain Monte Carlo NERFINISHED ⓘ Monte Carlo sampling ⓘ expectation propagation NERFINISHED ⓘ variational inference ⓘ |
| produces | posterior predictive distribution ⓘ |
| relatedTo |
Bayesian neural networks
NERFINISHED
ⓘ
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
NERFINISHED
ⓘ
posterior distribution over weights ⓘ prior distribution over weights ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Bayesian learning for neural networks Description of subject: 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.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Bayesian Learning for Neural Networks
this entity surface form:
Bayesian Learning for Neural Networks
this entity surface form:
Bayesian Learning for Neural Networks
this entity surface form:
Bayesian methods for neural networks