deep feedforward networks

E25122

artificial neural network architecture deep learning model

Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.

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All labels observed (3)

Label	Occurrences
deep feedforward networks canonical	1
feedforward neural networks	1
multilayer perceptrons	1

How this entity was disambiguated

This entity first appeared as the object of triple T197634 — 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: deep feedforward networks
Context triple: [Deep Learning (book), subject, deep feedforward networks]

A. “Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
B. “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.
C. Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
D. LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
E. Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
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: deep feedforward networks
Target entity description: Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.

A. “Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
B. “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.
C. Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
D. LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
E. Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
F. None of above. chosen

Statements (49)

Predicate	Object
instanceOf	artificial neural network architecture ⓘ deep learning model ⓘ
canUseActivationFunction	ReLU ⓘ leaky ReLU ⓘ sigmoid ⓘ softmax in output layer for classification ⓘ tanh ⓘ
canUseLossFunction	cross-entropy loss ⓘ mean squared error ⓘ
canUseOptimizer	Adam ⓘ RMSProp ⓘ SGD ⓘ
differsFrom	convolutional neural networks ⓘ recurrent neural networks ⓘ
hasAlternativeName	deep MLPs ⓘ deep feedforward neural networks ⓘ deep multilayer perceptrons ⓘ
hasComponent	input layer ⓘ one or more hidden layers ⓘ output layer ⓘ
hasKeyProperty	composed of layers of units with learnable weights ⓘ information flows in one direction ⓘ learn complex input–output mappings ⓘ multiple hidden layers ⓘ no recurrent connections ⓘ
hasProperty	depth enables hierarchical feature learning ⓘ differentiable with respect to parameters ⓘ feedforward computation from inputs to outputs ⓘ parameters organized in layers ⓘ universal function approximator under mild conditions ⓘ
introducedInContextOf	deep learning ⓘ
isSubclassOf	feedforward neural networks ⓘ
mayUse	batch normalization ⓘ residual connections ⓘ
regularizedBy	dropout ⓘ early stopping ⓘ weight decay ⓘ
requires	labeled training data for supervised tasks ⓘ
trainedBy	supervised learning ⓘ
trainedWith	backpropagation ⓘ gradient descent ⓘ mini-batch gradient descent ⓘ stochastic gradient descent ⓘ
usedFor	classification ⓘ function approximation ⓘ pattern recognition ⓘ regression ⓘ representation learning ⓘ
uses	nonlinear activation functions ⓘ

How these facts were elicited

Referenced by (3)

Full triples — surface form annotated when it differs from this entity's canonical label.

Deep Learning (book) → subject → deep feedforward networks ⓘ

ReLU → usedIn → feedforward neural networks ⓘ

linked to: deep feedforward networks

ReLU → usedIn → multilayer perceptrons ⓘ