“Large-Scale Machine Learning with Stochastic Gradient Descent”
E370247
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
All labels observed (2)
| Label | Occurrences |
|---|---|
| SGD (for stochastic gradient descent in the title) | 1 |
| “Large-Scale Machine Learning with Stochastic Gradient Descent” canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3542938 — 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 Machine Learning with Stochastic Gradient Descent” Context triple: [Léon Bottou, hasPublication, “Large-Scale Machine Learning with Stochastic Gradient Descent”]
-
A.
“Stochastic Gradient Descent Tricks”
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
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B.
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.
-
C.
Large-Scale Distributed Deep Networks
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.
-
D.
“The Tradeoffs of Large Scale Learning”
“The Tradeoffs of Large Scale Learning” is a research work by Léon Bottou that analyzes how to balance computational efficiency, data scale, and statistical performance in large-scale machine learning systems.
-
E.
“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.
- 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 Machine Learning with Stochastic Gradient Descent” Target entity description: “Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
-
A.
“Stochastic Gradient Descent Tricks”
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
-
B.
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.
-
C.
Large-Scale Distributed Deep Networks
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.
-
D.
“The Tradeoffs of Large Scale Learning”
“The Tradeoffs of Large Scale Learning” is a research work by Léon Bottou that analyzes how to balance computational efficiency, data scale, and statistical performance in large-scale machine learning systems.
-
E.
“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.
- F. None of above. chosen
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
research paper
ⓘ
scientific article ⓘ |
| advocates | use of stochastic gradient descent for large-scale problems ⓘ |
| analyzes |
computational complexity of stochastic gradient descent
ⓘ
convergence properties of stochastic gradient descent ⓘ |
| appliesTo |
classification problems
ⓘ
online learning scenarios ⓘ regression problems ⓘ supervised learning ⓘ |
| author | Léon Bottou ⓘ |
| comparesWith |
batch gradient descent
ⓘ
second-order optimization methods ⓘ |
| contribution |
analysis of asymptotic behavior of stochastic gradient descent
ⓘ
formal justification of stochastic gradient descent for large-scale learning ⓘ guidelines for practical use of stochastic gradient descent ⓘ |
| discusses |
data shuffling and sampling strategies
ⓘ
learning rate schedules ⓘ parallel and distributed implementations ⓘ practical implementation issues ⓘ regularization in stochastic gradient descent ⓘ trade-off between computation and statistical efficiency ⓘ |
| emphasizes |
memory efficiency
ⓘ
single-pass and few-pass algorithms over data ⓘ streaming data settings ⓘ |
| field |
machine learning
ⓘ
optimization ⓘ statistical learning ⓘ |
| focusesOn |
efficiency of stochastic gradient descent
ⓘ
incremental gradient methods ⓘ online learning ⓘ optimization for large datasets ⓘ scalability of learning algorithms ⓘ |
| hasAbbreviation |
“Large-Scale Machine Learning with Stochastic Gradient Descent”
self-linksurface differs
ⓘ
surface form:
SGD (for stochastic gradient descent in the title)
|
| influenced |
practical adoption of stochastic gradient descent in industry
ⓘ
research on large-scale optimization in machine learning ⓘ |
| isWidelyCited | true ⓘ |
| language | English ⓘ |
| mainTopic |
large-scale machine learning
ⓘ
stochastic gradient descent ⓘ |
| targetAudience |
data scientists
ⓘ
machine learning researchers ⓘ practitioners working with large datasets ⓘ |
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 Machine Learning with Stochastic Gradient Descent” Description of subject: “Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
“Large-Scale Machine Learning with Stochastic Gradient Descent”
→
hasAbbreviation
→
“Large-Scale Machine Learning with Stochastic Gradient Descent”
self-linksurface differs
ⓘ
subject surface form:
Large-Scale Machine Learning with Stochastic Gradient Descent
this entity surface form:
SGD (for stochastic gradient descent in the title)