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 501–525 of 1915 papers

Title	Date	Tasks	Status	Score
DiCENet: Dimension-wise Convolutions for Efficient Networks	Jun 8, 2019	image-classificationImage Classification	CodeCode Available	5
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks	Dec 17, 2024	Neural Architecture Search	CodeCode Available	5
Inner Ensemble Networks: Average Ensemble as an Effective Regularizer	Jun 15, 2020	Neural Architecture Search	CodeCode Available	5
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	5
Continual and Multi-Task Architecture Search	Jun 12, 2019	Continual LearningGeneral Classification	CodeCode Available	5
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search	Feb 2, 2024	Action RecognitionActivity Recognition	CodeCode Available	5
CONetV2: Efficient Auto-Channel Size Optimization for CNNs	Oct 13, 2021	Knowledge DistillationNeural Architecture Search	CodeCode Available	5
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion	Jan 4, 2024	Natural Language UnderstandingNeural Architecture Search	CodeCode Available	5
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation	Jul 18, 2022	Neural Architecture Searchvalid	CodeCode Available	5
CONet: Channel Optimization for Convolutional Neural Networks	Aug 15, 2021	Neural Architecture Search	CodeCode Available	5
AutoGAN: Neural Architecture Search for Generative Adversarial Networks	Aug 11, 2019	image-classificationImage Classification	CodeCode Available	5
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition	Nov 1, 2019	Language ModelingLanguage Modelling	CodeCode Available	5
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures	Feb 28, 2020	Neural Architecture Search	CodeCode Available	5
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network	May 6, 2024	Image SegmentationMedical Image Segmentation	CodeCode Available	5
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 Neural Architecture Search Image Classifiers via Ensemble Learning	Mar 14, 2019	Ensemble LearningImage Classification	CodeCode Available	5
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data	Oct 4, 2023	AutoMLHyperparameter Optimization	CodeCode Available	5
Adaptive hybrid activation function for deep neural networks	Apr 25, 2022	Activation Function Synthesisimage-classification	CodeCode Available	5
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism	Mar 15, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	5
How to 0wn the NAS in Your Spare Time	May 1, 2020	CPUMalware Detection	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
How to 0wn NAS in Your Spare Time	Feb 17, 2020	CPUDeep Learning	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
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML	Mar 2, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available	5
IRLAS: Inverse Reinforcement Learning for Architecture Search	Dec 13, 2018	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