methodology-20
Neural Architecture Search
3260 papers • 126 benchmarks • 313 datasets
Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures. Image Credit : NAS with Reinforcement Learning
(Image credit: Open Source)
Benchmarks
These leaderboards are used to track progress in neural-architecture-search-40
Trend
Dataset
Best Model
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ImageNet
ImageNet
NAS-Bench-201, ImageNet-16-120
NAS-Bench-201, ImageNet-16-120
CIFAR-10
Libraries
iUse these libraries to find neural-architecture-search-40 models and implementations
10 papers 2,915
Datasets
No datasets available.
