Neural Architecture Search (NAS)
A subfield of machine learning in which algorithms automatically search a predefined search space for optimal neural network architectures. It aims to replace the time-consuming manual network design process performed by researchers and gained prominence around 2017.
Core Facts
Timeline (last 90 days)
The search process of neural architecture search (NAS) itself requires consuming large amounts of computing power.
Data from a survey paper shows that more than 3,000 new model architectures were proposed in the NAS field over approximately five years.
Neural Architecture Search (NAS) emerged around 2017, with the core idea of using algorithms to automatically find optimal neural network structures within a predefined search space.
Early NAS work (such as NASNet and DARTS) was found, due to opaque search space design, to have discovered architectures that largely depended on specific proxy tasks and search budgets.
All Facts (5)
AutoML is a technology framework that automates traditional ML workflows such as feature selection, model selection, and hyperparameter tuning.
65%UnverifiedThe search process of neural architecture search (NAS) itself requires consuming large amounts of computing power.
50%UnverifiedData from a survey paper shows that more than 3,000 new model architectures were proposed in the NAS field over approximately five years.
50%UnverifiedNeural Architecture Search (NAS) emerged around 2017, with the core idea of using algorithms to automatically find optimal neural network structures within a predefined search space.
50%UnverifiedEarly NAS work (such as NASNet and DARTS) was found, due to opaque search space design, to have discovered architectures that largely depended on specific proxy tasks and search budgets.
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