Large-Scale Distributed Deep Networks
E238233
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.
All labels observed (4)
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
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: Large-Scale Distributed Deep Networks Description of subject: 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.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Large-Scale Distributed Deep Networks
linked to: Large-Scale Distributed Deep Networks
DistBelief
→
notablePublication
→
Building High-level Features Using Large Scale Unsupervised Learning
ⓘ
linked to: Large-Scale Distributed Deep Networks
linked to: Large-Scale Distributed Deep Networks