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 841–850 of 1915 papers

Title	Date	Tasks	Status	Hype
GraphPAS: Parallel Architecture Search for Graph Neural Networks	Dec 7, 2021	Neural Architecture Search	CodeCode Available	0
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers	Dec 7, 2021	Click-Through Rate PredictionEnsemble Learning	—Unverified	0
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition	Dec 7, 2021	Deep LearningLand Cover Classification	—Unverified	0
Manas: Mining Software Repositories to Assist AutoML	Dec 6, 2021	AutoMLGPU	CodeCode Available	0
Exploring Complicated Search Spaces with Interleaving-Free Sampling	Dec 5, 2021	Neural Architecture Search	—Unverified	0
Data-Free Neural Architecture Search via Recursive Label Calibration	Dec 3, 2021	DiversityNeural Architecture Search	—Unverified	0
The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees	Dec 3, 2021	Neural Architecture Search	—Unverified	0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search	Dec 1, 2021	BIG-bench Machine Learningimage-classification	—Unverified	0
Training BatchNorm Only in Neural Architecture Search and Beyond	Dec 1, 2021	FairnessNeural Architecture Search	—Unverified	0
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning	Dec 1, 2021	Meta-LearningNeural Architecture Search	CodeCode Available	1

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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