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 151–175 of 1915 papers

Title	Date	Tasks	Status	Hype	Score
AutoKE: An automatic knowledge embedding framework for scientific machine learning	May 11, 2022	BIG-bench Machine LearningNeural Architecture Search	CodeCode Available	1	5
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective	Dec 29, 2021	feature selectionNeural Architecture Search	CodeCode Available	1	5
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning	Jan 28, 2023	Bayesian OptimisationNeural Architecture Search	CodeCode Available	1	5
Differentiable Model Scaling using Differentiable Topk	May 12, 2024	GPUimage-classification	CodeCode Available	1	5
AutoGL: A Library for Automated Graph Learning	Apr 11, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available	1	5
A Study on Encodings for Neural Architecture Search	Jul 9, 2020	Neural Architecture Search	CodeCode Available	1	5
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions	Jan 4, 2022	BIG-bench Machine LearningGraph Learning	CodeCode Available	1	5
Discretization-Aware Architecture Search	Jul 7, 2020	image-classificationImage Classification	CodeCode Available	1	5
AutoSpeech: Neural Architecture Search for Speaker Recognition	May 7, 2020	image-classificationImage Classification	CodeCode Available	1	5
Bayesian Model Selection, the Marginal Likelihood, and Generalization	Feb 23, 2022	modelModel Selection	CodeCode Available	1	5
DARTS: Differentiable Architecture Search	Jun 24, 2018	General Classificationimage-classification	CodeCode Available	1	5
Efficient Architecture Search for Diverse Tasks	Apr 15, 2022	AutoMLNeural Architecture Search	CodeCode Available	1	5
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization	May 3, 2021	AutoMLHyperparameter Optimization	CodeCode Available	1	5
Efficient Forward Architecture Search	May 31, 2019	feature selectionGPU	CodeCode Available	1	5
Dataset Condensation with Gradient Matching	Jun 10, 2020	Continual LearningDataset Condensation	CodeCode Available	1	5
Contrastive Neural Architecture Search with Neural Architecture Comparators	Mar 8, 2021	Neural Architecture Search	CodeCode Available	1	5
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1	5
EfficientTDNN: Efficient Architecture Search for Speaker Recognition	Mar 25, 2021	Data AugmentationNetwork Pruning	CodeCode Available	1	5
ConvNet Architecture Search for Spatiotemporal Feature Learning	Aug 16, 2017	Action ClassificationAction Recognition	CodeCode Available	1	5
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search	Oct 25, 2019	Bayesian OptimizationHyperparameter Optimization	CodeCode Available	1	5
AtomNAS: Fine-Grained End-to-End Neural Architecture Search	Dec 20, 2019	Neural Architecture Search	CodeCode Available	1	5
Searching a Compact Architecture for Robust Multi-Exposure Image Fusion	May 20, 2023	Multi-Exposure Image FusionNeural Architecture Search	CodeCode Available	1	5
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search	Oct 12, 2022	Neural Architecture Search	CodeCode Available	1	5
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices	Oct 11, 2023	Neural Architecture Search	CodeCode Available	1	5
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars	Nov 3, 2022	AutoMLBayesian Optimization	CodeCode Available	1	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