ProxylessNAS
E899018
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| ProxylessNAS canonical | 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
NERFINISHED
ⓘ
DARTS NERFINISHED ⓘ NASNet NERFINISHED ⓘ |
| doesNotUse |
proxy networks
ⓘ
proxy tasks ⓘ |
| evaluatedOn | ImageNet NERFINISHED ⓘ |
| field |
computer vision
ⓘ
machine learning ⓘ |
| fullName | Proxyless Neural Architecture Search NERFINISHED ⓘ |
| 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
NERFINISHED
ⓘ
Ligeng Zhu NERFINISHED ⓘ Song Han NERFINISHED ⓘ |
| publicationYear | 2019 ⓘ |
| publishedIn | International Conference on Learning Representations NERFINISHED ⓘ |
| relatedTo |
efficient deep learning
ⓘ
model compression ⓘ neural architecture search NERFINISHED ⓘ |
| 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
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: ProxylessNAS Description of subject: 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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.