“The Tradeoffs of Large Scale Learning”
E367294
“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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| “The Tradeoffs of Large Scale Learning” canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3542941 — 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: “The Tradeoffs of Large Scale Learning” Context triple: [Léon Bottou, hasPublication, “The Tradeoffs of Large Scale Learning”]
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A.
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.
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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.
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C.
“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.
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D.
Probably Approximately Correct learning (PAC learning)
Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
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E.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
- 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: “The Tradeoffs of Large Scale Learning” Target entity description: “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.
-
A.
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.
-
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.
“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.
-
D.
Probably Approximately Correct learning (PAC learning)
Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
-
E.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
- F. None of above. chosen
Statements (43)
| Predicate | Object |
|---|---|
| instanceOf |
research paper
ⓘ
scientific article ⓘ |
| addresses |
computational cost of training
ⓘ
convergence properties of large-scale optimization ⓘ design of learning algorithms for very large datasets ⓘ memory constraints in learning algorithms ⓘ statistical efficiency of learning procedures ⓘ |
| author | Léon Bottou ⓘ |
| concludes |
computational budget should guide algorithm design
ⓘ
exact optimization may be unnecessary for good generalization ⓘ simple algorithms can perform well at large scale ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ statistics ⓘ |
| focusesOn |
approximate optimization methods
ⓘ
incremental learning algorithms ⓘ practical constraints in large-scale learning systems ⓘ tradeoff between computation and data size ⓘ tradeoff between model complexity and scalability ⓘ tradeoff between training time and accuracy ⓘ |
| hasInfluenceOn |
design of industrial-scale learning systems
ⓘ
development of online learning algorithms ⓘ practical deployment of machine learning in large data settings ⓘ research on scalable optimization methods ⓘ |
| language | English ⓘ |
| mainTopic |
batch learning
ⓘ
computational efficiency in machine learning ⓘ large-scale machine learning ⓘ learning theory ⓘ online learning ⓘ optimization in large-scale learning ⓘ scalability of learning algorithms ⓘ statistical performance in machine learning ⓘ stochastic gradient descent ⓘ |
| proposes |
guidelines for balancing computation and statistics
ⓘ
heuristics for large-scale optimization ⓘ |
| relatedTo |
distributed learning
ⓘ
empirical risk minimization ⓘ generalization error ⓘ learning curves ⓘ parallel computation in machine learning ⓘ regularization in large-scale learning ⓘ stochastic approximation ⓘ |
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: “The Tradeoffs of Large Scale Learning” Description of subject: “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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.