ProxylessNAS

E899018

NAS algorithm deep learning technique neural architecture search method

ProxylessNAS is a neural architecture search method that directly optimizes neural network architectures on target tasks and hardware without relying on proxy models, enabling efficient and hardware-aware network design.

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

All labels observed (3)

Label	Occurrences
ProxylessNAS canonical	2
Efficient Neural Architecture Search	1
Proxyless Neural Architecture Search	1

How this entity was disambiguated

This entity first appeared as the object of triple T11003097 — 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: ProxylessNAS
Context triple: [Neural Architecture Search, notableMethod, ProxylessNAS]

A. Neural Architecture Search
Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
B. Learning Transferable Architectures for Scalable Image Recognition
"Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
C. MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
D. ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
E. SqueezeNet
SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
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: ProxylessNAS
Target entity description: ProxylessNAS is a neural architecture search method that directly optimizes neural network architectures on target tasks and hardware without relying on proxy models, enabling efficient and hardware-aware network design.

A. Neural Architecture Search
Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
B. Learning Transferable Architectures for Scalable Image Recognition
"Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
C. MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
D. ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
E. SqueezeNet
SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
F. None of above. chosen

Statements (47)

Predicate	Object
instanceOf	NAS algorithm ⓘ deep learning technique ⓘ neural architecture search method ⓘ
aimsTo	directly optimize architectures on target hardware ⓘ directly optimize architectures on target tasks ⓘ
codeAvailability	open source implementation ⓘ
comparedWith	AmoebaNet ⓘ DARTS ⓘ NASNet ⓘ
doesNotUse	proxy networks ⓘ proxy tasks ⓘ
evaluatedOn	ImageNet ⓘ
field	computer vision ⓘ machine learning ⓘ
fullName	Proxyless Neural Architecture Search ⓘ linked to: ProxylessNAS
handles	discrete architecture choices via relaxation ⓘ
hardwarePlatform	CPUs ⓘ GPUs ⓘ mobile phones ⓘ
improvesOn	deployment realism ⓘ search efficiency ⓘ
keyIdea	jointly consider accuracy and hardware efficiency ⓘ remove proxy gap between search and deployment ⓘ
latencyModel	measured on real hardware ⓘ
objective	learn architecture parameters ⓘ learn network weights ⓘ
optimizedFor	target hardware ⓘ target tasks ⓘ
proposedBy	Han Cai ⓘ Ligeng Zhu ⓘ Song Han ⓘ
publicationYear	2019 ⓘ
publishedIn	International Conference on Learning Representations ⓘ linked to: ICLR
relatedTo	efficient deep learning ⓘ model compression ⓘ neural architecture search ⓘ linked to: Neural Architecture Search
searchSpace	ResNet-like architectures ⓘ mobile CNN architectures ⓘ
searchStrategy	differentiable NAS ⓘ
supports	hardware-aware neural architecture search ⓘ
targetMetric	FLOPs ⓘ accuracy ⓘ latency ⓘ model size ⓘ
uses	gradient-based optimization ⓘ path-level binarization ⓘ stochastic binary gates ⓘ

How these facts were elicited

Referenced by (4)

Full triples — surface form annotated when it differs from this entity's canonical label.

Neural Architecture Search → notableMethod → ProxylessNAS ⓘ

DARTS → inspired → ProxylessNAS ⓘ

ENAS → fullName → Efficient Neural Architecture Search ⓘ

linked to: ProxylessNAS

ProxylessNAS → fullName → Proxyless Neural Architecture Search ⓘ