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 551–575 of 1915 papers

Title	Date	Tasks	Status	Score
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NAS	Jun 13, 2022	Neural Architecture Search	CodeCode Available	5
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space	Jan 1, 2021	image-classificationImage Classification	CodeCode Available	5
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions	May 18, 2023	Neural Architecture SearchTransfer Learning	CodeCode Available	5
Latency-Aware Differentiable Neural Architecture Search	Jan 17, 2020	Neural Architecture Search	CodeCode Available	5
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism	Mar 15, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	5
CAP: A Context-Aware Neural Predictor for NAS	Jun 4, 2024	Neural Architecture Search	CodeCode Available	5
A Transformer-based Neural Architecture Search Method	May 2, 2025	DecoderNeural Architecture Search	CodeCode Available	5
How to 0wn the NAS in Your Spare Time	May 1, 2020	CPUMalware Detection	CodeCode Available	5
How to 0wn NAS in Your Spare Time	Feb 17, 2020	CPUDeep Learning	CodeCode Available	5
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders	Jan 22, 2019	Image CompressionMS-SSIM	CodeCode Available	5
Butterfly Transform: An Efficient FFT Based Neural Architecture Design	Jun 5, 2019	Neural Architecture Search	CodeCode Available	5
ATOM: Attention Mixer for Efficient Dataset Distillation	May 2, 2024	Dataset DistillationNeural Architecture Search	CodeCode Available	5
Building Optimal Neural Architectures using Interpretable Knowledge	Mar 20, 2024	image-classificationImage Classification	CodeCode Available	5
Homogeneous Architecture Augmentation for Neural Predictor	Jul 28, 2021	Neural Architecture Search	CodeCode Available	5
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens	May 29, 2020	GPUNeural Architecture Search	CodeCode Available	5
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints	May 12, 2019	General Classificationimage-classification	CodeCode Available	5
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search	Jan 13, 2020	Knowledge DistillationNeural Architecture Search	CodeCode Available	5
HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization	Nov 18, 2022	Metaheuristic OptimizationNeural Architecture Search	CodeCode Available	5
How does topology of neural architectures impact gradient propagation and model performance?	Jun 16, 2021	Model CompressionNeural Architecture Search	CodeCode Available	5
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image Classification	Oct 26, 2023	Computational EfficiencyDecoder	CodeCode Available	5
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020	Apr 6, 2022	AutoMLGPU	CodeCode Available	5
Heterogeneous Graph Neural Architecture Search with GPT-4	Dec 14, 2023	Neural Architecture Search	CodeCode Available	5
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor	Nov 1, 2022	GPUNeural Architecture Search	CodeCode Available	5
Hardware Aware Neural Network Architectures using FbNet	Jun 17, 2019	BenchmarkingNeural Architecture Search	CodeCode Available	5
Hardware/Software Co-Exploration of Neural Architectures	Jul 6, 2019	Neural Architecture SearchReinforcement Learning	CodeCode Available	5

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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