“Large-Scale Machine Learning with Stochastic Gradient Descent”

E370247

research paper scientific article

“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.

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

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.
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	SGD (for stochastic gradient descent in the title) ⓘ linked to: “Large-Scale Machine Learning with Stochastic Gradient Descent”
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 ⓘ