AutoML: A Survey of the State-of-the-Art
E260049
"AutoML: A Survey of the State-of-the-Art" is a comprehensive academic survey paper that reviews and synthesizes methods, tools, and challenges in automated machine learning, including model selection, hyperparameter optimization, and neural architecture search.
All labels observed (1)
| Label | Occurrences |
|---|---|
| AutoML: A Survey of the State-of-the-Art canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2373682 — 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: AutoML: A Survey of the State-of-the-Art Context triple: [Quoc V. Le, coAuthorOf, AutoML: A Survey of the State-of-the-Art]
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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.
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B.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
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C.
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.
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D.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
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E.
Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
- 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: AutoML: A Survey of the State-of-the-Art Target entity description: "AutoML: A Survey of the State-of-the-Art" is a comprehensive academic survey paper that reviews and synthesizes methods, tools, and challenges in automated machine learning, including model selection, hyperparameter optimization, and neural architecture search.
-
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.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
C.
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.
-
D.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
-
E.
Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
academic survey paper
ⓘ
review article ⓘ scientific article ⓘ |
| addresses |
challenges in hyperparameter optimization
ⓘ
challenges in model selection automation ⓘ challenges in neural architecture search ⓘ computational cost of AutoML ⓘ evaluation and benchmarking of AutoML systems ⓘ scalability issues in AutoML ⓘ |
| aimsTo |
identify open problems in AutoML
ⓘ
provide a comprehensive overview of AutoML ⓘ synthesize methods and tools in AutoML ⓘ |
| describes |
end-to-end AutoML systems
ⓘ
methods for automated hyperparameter tuning ⓘ methods for automated model selection ⓘ methods for neural architecture search ⓘ performance estimation strategies in AutoML ⓘ search algorithms for AutoML ⓘ |
| field | automated machine learning ⓘ |
| fieldOfStudy |
artificial intelligence
ⓘ
computer science ⓘ machine learning ⓘ |
| focusesOn |
practical aspects of deploying AutoML
ⓘ
theoretical aspects of AutoML methods ⓘ |
| hasForm |
PDF
ⓘ
online article ⓘ |
| intendedFor |
practitioners using AutoML systems
ⓘ
researchers in machine learning ⓘ students studying automated machine learning ⓘ |
| language | English ⓘ |
| surveys |
applications of AutoML
ⓘ
existing AutoML software ⓘ state-of-the-art AutoML approaches ⓘ |
| topic |
AutoML
ⓘ
AutoML benchmarks ⓘ AutoML tools and frameworks ⓘ Bayesian optimization ⓘ black-box optimization ⓘ challenges in AutoML ⓘ evaluation strategies in AutoML ⓘ future directions in AutoML ⓘ hyperparameter optimization ⓘ meta-learning ⓘ model selection ⓘ neural architecture search ⓘ pipeline search ⓘ search space design ⓘ |
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: AutoML: A Survey of the State-of-the-Art Description of subject: "AutoML: A Survey of the State-of-the-Art" is a comprehensive academic survey paper that reviews and synthesizes methods, tools, and challenges in automated machine learning, including model selection, hyperparameter optimization, and neural architecture search.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.