Large-Scale Distributed Deep Networks

E238233

deep learning method research paper scientific publication

Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (4)

Label	Occurrences
Building High-level Features Using Large Scale Unsupervised Learning	2
ImageNet Classification with Deep Convolutional Neural Networks	1
Large Scale Distributed Deep Networks	1
Large-Scale Distributed Deep Networks canonical	1

How this entity was disambiguated

This entity first appeared as the object of triple T2139452 — 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: Large-Scale Distributed Deep Networks
Context triple: [Jeff Dean, notablePublication, Large-Scale Distributed Deep Networks]

A. Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
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. Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
D. 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.
E. Deep belief networks
Deep belief networks are a class of deep generative neural network models composed of stacked layers of latent variables, typically built from restricted Boltzmann machines, used for unsupervised feature learning and representation.
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: Large-Scale Distributed Deep Networks
Target entity description: Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.

A. Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
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. Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
D. 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.
E. Deep belief networks
Deep belief networks are a class of deep generative neural network models composed of stacked layers of latent variables, typically built from restricted Boltzmann machines, used for unsupervised feature learning and representation.
F. None of above. chosen

Statements (37)

Predicate	Object
instanceOf	deep learning method ⓘ research paper ⓘ scientific publication ⓘ
aimsTo	reduce training time for deep neural networks ⓘ scale deep learning to very large datasets ⓘ scale deep learning to very large models ⓘ
appliedIn	industry-scale machine learning systems ⓘ large-scale computer vision systems ⓘ large-scale recommendation systems ⓘ large-scale speech recognition systems ⓘ
characteristic	highly scalable training architecture ⓘ support for very large datasets ⓘ support for very large models ⓘ
contribution	demonstrated scalability of deep learning to large datasets and models ⓘ introduced methods for training deep networks across distributed infrastructure ⓘ
enables	efficient training of deep neural networks on large clusters ⓘ modern large-scale AI systems ⓘ scaling deep learning beyond single-machine limits ⓘ training on distributed clusters of machines ⓘ
field	deep learning ⓘ distributed computing ⓘ machine learning ⓘ
focusesOn	large-scale distributed training ⓘ scalable deep learning ⓘ training deep neural networks ⓘ
impact	enabled practical training of deep models on industrial-scale data ⓘ influenced design of large-scale AI training systems ⓘ
recognizedAs	foundational for modern large-scale AI training frameworks ⓘ seminal work in large-scale deep learning ⓘ
relatedTo	cluster-based neural network training ⓘ distributed stochastic gradient descent ⓘ large-scale AI infrastructure ⓘ parallel deep learning ⓘ
uses	data parallelism ⓘ distributed computing infrastructure ⓘ model parallelism ⓘ parallelization of neural network training ⓘ

How these facts were elicited

Referenced by (5)

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

Jeff Dean → notablePublication → Large-Scale Distributed Deep Networks ⓘ

Quoc V. Le → coAuthorOf → Large-Scale Distributed Deep Networks ⓘ

this entity surface form: Building High-level Features Using Large Scale Unsupervised Learning

AlexNet → introducedInPaper → Large-Scale Distributed Deep Networks ⓘ

this entity surface form: ImageNet Classification with Deep Convolutional Neural Networks

DistBelief → notablePublication → Large-Scale Distributed Deep Networks ⓘ