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 1826–1850 of 1915 papers

Title	Date	Tasks	Status	Hype
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search	Jan 1, 2019	Evolutionary AlgorithmsImage Classification	—Unverified	0
Katib: A Distributed General AutoML Platform on Kubernetes	Jan 1, 2019	AutoMLHyperparameter Optimization	—Unverified	0
Neural Architecture Search Over a Graph Search Space	Dec 27, 2018	image-classificationImage Classification	—Unverified	0
SNAS: Stochastic Neural Architecture Search	Dec 24, 2018	Neural Architecture Searchreinforcement-learning	CodeCode Available	1
Meta Architecture Search	Dec 22, 2018	Bayesian InferenceFew-Shot Learning	CodeCode Available	0
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation	Dec 21, 2018	Bayesian OptimizationCPU	CodeCode Available	1
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks	Dec 20, 2018	Machine TranslationNeural Architecture Search	—Unverified	0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search	Dec 17, 2018	General ClassificationMeta Reinforcement Learning	—Unverified	0
Evolutionary Neural Architecture Search for Image Restoration	Dec 14, 2018	GPUimage-classification	—Unverified	0
IRLAS: Inverse Reinforcement Learning for Architecture Search	Dec 13, 2018	Neural Architecture Searchreinforcement-learning	CodeCode Available	0
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search	Dec 9, 2018	GPUImage Classification	CodeCode Available	1
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search	Dec 7, 2018	General ClassificationNeural Architecture Search	—Unverified	0
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware	Dec 2, 2018	GPUImage Classification	CodeCode Available	2
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization	Dec 2, 2018	Network PruningNeural Architecture Search	CodeCode Available	0
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search	Nov 30, 2018	Neural Architecture SearchQuantization	—Unverified	0
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks	Nov 29, 2018	General Classificationimage-classification	—Unverified	0
GP-CNAS: Convolutional Neural Network Architecture Search with Genetic Programming	Nov 26, 2018	DiversityNeural Architecture Search	—Unverified	0
InstaNAS: Instance-aware Neural Architecture Search	Nov 26, 2018	DiversityNeural Architecture Search	CodeCode Available	0
Evolutionary-Neural Hybrid Agents for Architecture Search	Nov 24, 2018	Deep Reinforcement LearningEvolutionary Algorithms	—Unverified	0
Joint Neural Architecture Search and Quantization	Nov 23, 2018	Model CompressionNeural Architecture Search	—Unverified	0
Deep Active Learning with a Neural Architecture Search	Nov 19, 2018	Active LearningNeural Architecture Search	CodeCode Available	0
Stochastic Adaptive Neural Architecture Search for Keyword Spotting	Nov 16, 2018	Keyword SpottingNeural Architecture Search	CodeCode Available	0
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization	Nov 5, 2018	Neural Architecture SearchReinforcement Learning	CodeCode Available	0
Automated Machine Learning: From Principles to Practices	Oct 31, 2018	AutoMLBIG-bench Machine Learning	CodeCode Available	0
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints	Oct 27, 2018	6D Pose Estimation using RGBDIndustrial Robots	CodeCode Available	0

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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