Neural Architecture Search

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1851–1875 of 1915 papers

Title	Date	Tasks	Status
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost	May 9, 2024	Auxiliary LearningNeural Architecture Search	CodeCode Available
Automatic and effective discovery of quantum kernels	Sep 22, 2022	AutoMLCombinatorial Optimization	CodeCode Available
RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge Devices	Sep 27, 2024	Neural Architecture Search	CodeCode Available
Design Principle Transfer in Neural Architecture Search via Large Language Models	Aug 21, 2024	Language ModellingLarge Language Model	CodeCode Available
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning	Apr 30, 2025	Graph Neural NetworkGraph Representation Learning	CodeCode Available
Towards modular and programmable architecture search	Sep 30, 2019	Hyperparameter OptimizationNeural Architecture Search	CodeCode Available
Auto-tuning of Deep Neural Networks by Conflicting Layer Removal	Mar 7, 2021	Neural Architecture Search	CodeCode Available
AutoST: Training-free Neural Architecture Search for Spiking Transformers	Jul 1, 2023	Neural Architecture Search	CodeCode Available
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge	Sep 1, 2019	Deep LearningNeural Architecture Search	CodeCode Available
Stage-Wise Neural Architecture Search	Apr 23, 2020	Neural Architecture Search	CodeCode Available
Sub-Architecture Ensemble Pruning in Neural Architecture Search	Oct 1, 2019	DiversityEnsemble Learning	CodeCode Available
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks	Jan 8, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Densely Connected Search Space for More Flexible Neural Architecture Search	Jun 23, 2019	Image ClassificationNeural Architecture Search	CodeCode Available
ABC-Di: Approximate Bayesian Computation for Discrete Data	Oct 19, 2020	Neural Architecture Searchvalid	CodeCode Available
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks	Oct 11, 2022	Adversarial RobustnessGPU	CodeCode Available
Neural Architecture Search for Sentence Classification with BERT	Mar 27, 2024	AutoMLClassification	CodeCode Available
Towards NNGP-guided Neural Architecture Search	Nov 11, 2020	Neural Architecture Search	CodeCode Available
Adapting Neural Architectures Between Domains	Dec 1, 2020	Domain AdaptationGeneralization Bounds	CodeCode Available
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?	Mar 28, 2022	Neural Architecture Search	CodeCode Available
Neural Architecture Search for Visual Anomaly Segmentation	Apr 18, 2023	Anomaly SegmentationNeural Architecture Search	CodeCode Available
Fisher Task Distance and Its Application in Neural Architecture Search	Mar 23, 2021	Neural Architecture SearchTransfer Learning	CodeCode Available
Improved Automated Machine Learning from Transfer Learning	Feb 27, 2021	BIG-bench Machine LearningGPU	CodeCode Available
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles	Apr 30, 2019	CPUGPU	CodeCode Available
Understanding and Exploring the Network with Stochastic Architectures	Dec 1, 2020	Neural Architecture Search	CodeCode Available
Supernet Training for Federated Image Classification under System Heterogeneity	Jun 3, 2022	ClassificationFederated Learning	CodeCode Available

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All datasets ImageNet NAS-Bench-201, ImageNet-16-120 CIFAR-10 NAS-Bench-201, CIFAR-100 NAS-Bench-201, CIFAR-10 CIFAR-10 Image Classification CIFAR-100 NATS-Bench Topology, CIFAR-10 NATS-Bench Topology, CIFAR-100 NATS-Bench Topology, ImageNet16-120 Food-101 NAS-Bench-101

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SPOS (ProxylessNAS (GPU) latency)	Accuracy	75.3	—	Unverified
2	SPOS (FBNet-C latency)	Accuracy	75.1	—	Unverified
3	SPOS (block search + channel search)	Accuracy	74.7	—	Unverified
4	MUXNet-xs	Top-1 Error Rate	33.3	—	Unverified
5	FBNetV2-F1	Top-1 Error Rate	31.7	—	Unverified
6	LayerNAS-60M	Top-1 Error Rate	31	—	Unverified
7	NASGEP	Top-1 Error Rate	29.51	—	Unverified
8	MUXNet-s	Top-1 Error Rate	28.4	—	Unverified
9	NN-MASS-A	Top-1 Error Rate	27.1	—	Unverified
10	FBNetV2-F3	Top-1 Error Rate	26.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CR-LSO	Accuracy (Test)	46.98	—	Unverified
2	Shapley-NAS	Accuracy (Test)	46.85	—	Unverified
3	β-RDARTS-L2	Accuracy (Test)	46.71	—	Unverified
4	β-SDARTS-RS	Accuracy (Test)	46.71	—	Unverified
5	ASE-NAS+	Accuracy (Val)	46.66	—	Unverified
6	NAR	Accuracy (Test)	46.66	—	Unverified
7	BaLeNAS-TF	Accuracy (Test)	46.54	—	Unverified
8	AG-Net	Accuracy (Test)	46.42	—	Unverified
9	Local search	Accuracy (Test)	46.38	—	Unverified
10	NASBOT	Accuracy (Test)	46.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Balanced Mixture	Accuracy (% )	91.55	—	Unverified
2	GDAS	Top-1 Error Rate	3.4	—	Unverified
3	Bonsai-Net	Top-1 Error Rate	3.35	—	Unverified
4	Net2 (2)	Top-1 Error Rate	3.3	—	Unverified
5	μDARTS	Top-1 Error Rate	3.28	—	Unverified
6	NN-MASS- CIFAR-C	Top-1 Error Rate	3.18	—	Unverified
7	NN-MASS- CIFAR-A	Top-1 Error Rate	3	—	Unverified
8	DARTS (first order)	Top-1 Error Rate	3	—	Unverified
9	NASGEP	Top-1 Error Rate	2.82	—	Unverified
10	AlphaX-1 (cutout NASNet)	Top-1 Error Rate	2.82	—	Unverified