Neural Architecture Search

E260047

automated machine learning technique hyperparameter optimization approach neural network design method

Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.

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

All labels observed (5)

Label	Occurrences
Neural Architecture Search with Reinforcement Learning	4
Differentiable Architecture Search	1
Neural Architecture Search canonical	1
neural architecture search	1
neuroevolution of augmenting topologies for control tasks	1

How this entity was disambiguated

This entity first appeared as the object of triple T2373678 — 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: Neural Architecture Search
Context triple: [Quoc V. Le, knownFor, 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. Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
D. Neural Filters
Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
E. AlphaGo Zero
AlphaGo Zero is DeepMind's advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human data.
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: Neural Architecture Search
Target entity description: Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.

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. Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
D. Neural Filters
Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
E. AlphaGo Zero
AlphaGo Zero is DeepMind's advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human data.
F. None of above. chosen

Statements (70)

Predicate	Object
instanceOf	automated machine learning technique ⓘ hyperparameter optimization approach ⓘ neural network design method ⓘ
aimsTo	automate neural network architecture design ⓘ optimize neural network architectures ⓘ reduce human intervention in architecture design ⓘ
appliedTo	image classification ⓘ natural language processing ⓘ object detection ⓘ semantic segmentation ⓘ speech recognition ⓘ tabular data modeling ⓘ time series forecasting ⓘ
canOptimize	activation functions ⓘ cell structures ⓘ connectivity patterns ⓘ kernel sizes ⓘ layer types ⓘ network depth ⓘ normalization layers ⓘ number of layers ⓘ skip connections ⓘ width of layers ⓘ
canOptimizeFor	accuracy ⓘ energy consumption ⓘ latency ⓘ memory footprint ⓘ model size ⓘ
emergedAround	mid-2010s ⓘ
fieldOfStudy	deep learning ⓘ machine learning ⓘ
goal	adapt architectures to specific hardware constraints ⓘ discover high-performing architectures ⓘ reduce manual architecture engineering effort ⓘ
hasChallenge	evaluation cost of candidate architectures ⓘ high computational cost ⓘ large search space ⓘ overfitting to validation set ⓘ transferability of found architectures ⓘ
hasComponent	performance estimation strategy ⓘ search space ⓘ search strategy ⓘ
hasVariant	evolutionary NAS ⓘ gradient-based NAS ⓘ hardware-aware NAS ⓘ multi-objective NAS ⓘ one-shot NAS ⓘ reinforcement learning based NAS ⓘ
notableMethod	AmoebaNet ⓘ DARTS ⓘ ENAS ⓘ NASNet ⓘ ProxylessNAS ⓘ
operatesOn	architecture hyperparameters ⓘ neural network architectures ⓘ
relatedTo	AutoML ⓘ architecture search space design ⓘ hyperparameter optimization ⓘ meta-learning ⓘ model compression ⓘ neural network pruning ⓘ
usedIn	industrial machine learning systems ⓘ research on automated deep learning ⓘ
uses	Bayesian optimization ⓘ evolutionary algorithms ⓘ gradient-based optimization ⓘ optimization algorithms ⓘ performance prediction models ⓘ reinforcement learning ⓘ search algorithms ⓘ

How these facts were elicited