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 1526–1550 of 1915 papers

Title	Date	Tasks	Status
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS	Nov 22, 2024	Neural Architecture Search	CodeCode Available
Contrastive Self-supervised Neural Architecture Search	Feb 21, 2021	Neural Architecture SearchSelf-Supervised Learning	CodeCode Available
Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search	Jun 5, 2023	Evolutionary AlgorithmsNeural Architecture Search	CodeCode Available
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints	Oct 27, 2018	6D Pose Estimation using RGBDIndustrial Robots	CodeCode Available
Inner Ensemble Networks: Average Ensemble as an Effective Regularizer	Jun 15, 2020	Neural Architecture Search	CodeCode Available
Insights from the Use of Previously Unseen Neural Architecture Search Datasets	Apr 2, 2024	Neural Architecture Search	CodeCode Available
InstaNAS: Instance-aware Neural Architecture Search	Nov 26, 2018	DiversityNeural Architecture Search	CodeCode Available
Continual and Multi-Task Architecture Search	Jun 12, 2019	Continual LearningGeneral Classification	CodeCode Available
Task-Aware Neural Architecture Search	Oct 27, 2020	Neural Architecture Search	CodeCode Available
GraphPAS: Parallel Architecture Search for Graph Neural Networks	Dec 7, 2021	Neural Architecture Search	CodeCode Available
On Redundancy and Diversity in Cell-based Neural Architecture Search	Mar 16, 2022	DiversityNeural Architecture Search	CodeCode Available
On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency	Mar 27, 2024	ClassificationENF (Electric Network Frequency) Classification	CodeCode Available
Inter-layer Transition in Neural Architecture Search	Nov 30, 2020	Neural Architecture Search	CodeCode Available
On the Adversarial Transferability of Generalized "Skip Connections"	Oct 11, 2024	Neural Architecture Search	CodeCode Available
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions	May 18, 2023	Neural Architecture SearchTransfer Learning	CodeCode Available
Auto-nnU-Net: Towards Automated Medical Image Segmentation	May 22, 2025	AutoMLComputational Efficiency	CodeCode Available
TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices	Mar 14, 2022	Neural Architecture SearchQuantization	CodeCode Available
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights	Feb 7, 2025	image-classificationImage Classification	CodeCode Available
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance	Sep 22, 2024	AutoMLBenchmarking	CodeCode Available
BINAS: Bilinear Interpretable Neural Architecture Search	Oct 24, 2021	Neural Architecture Search	CodeCode Available
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems	Apr 1, 2022	Click-Through Rate PredictionFeature Engineering	CodeCode Available
IRLAS: Inverse Reinforcement Learning for Architecture Search	Dec 13, 2018	Neural Architecture Searchreinforcement-learning	CodeCode Available
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning	Apr 22, 2019	General ClassificationInductive Learning	CodeCode Available
CONetV2: Efficient Auto-Channel Size Optimization for CNNs	Oct 13, 2021	Knowledge DistillationNeural Architecture Search	CodeCode Available
On the Security Risks of AutoML	Oct 12, 2021	AutoMLModel Poisoning	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