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 1651–1675 of 1915 papers

Title	Date	Tasks	Status	Hype
Searching for A Robust Neural Architecture in Four GPU Hours	Oct 10, 2019	GPUNeural Architecture Search	CodeCode Available	1
Improving One-shot NAS by Suppressing the Posterior Fading	Oct 6, 2019	GPUNeural Architecture Search	—Unverified	0
Fast and Practical Neural Architecture Search	Oct 1, 2019	DiversityGPU	CodeCode Available	0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification	Oct 1, 2019	ClassificationGeneral Classification	—Unverified	0
Blending Diverse Physical Priors with Neural Networks	Oct 1, 2019	Neural Architecture Search	CodeCode Available	0
Sub-Architecture Ensemble Pruning in Neural Architecture Search	Oct 1, 2019	DiversityEnsemble Learning	CodeCode Available	0
ReNAS:Relativistic Evaluation of Neural Architecture Search	Sep 30, 2019	Neural Architecture Search	CodeCode Available	1
A Quantile-based Approach for Hyperparameter Transfer Learning	Sep 30, 2019	Bayesian OptimizationHyperparameter Optimization	—Unverified	0
Towards modular and programmable architecture search	Sep 30, 2019	Hyperparameter OptimizationNeural Architecture Search	CodeCode Available	0
Hierarchical Neural Architecture Search via Operator Clustering	Sep 26, 2019	ClusteringNeural Architecture Search	CodeCode Available	0
Exascale Deep Learning to Accelerate Cancer Research	Sep 26, 2019	Deep LearningNeural Architecture Search	—Unverified	0
CNAS: Channel-Level Neural Architecture Search	Sep 25, 2019	Neural Architecture Search	—Unverified	0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI	Sep 25, 2019	Neural Architecture Search	—Unverified	0
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search	Sep 25, 2019	Bayesian OptimizationNeural Architecture Search	—Unverified	0
Neural Operator Search	Sep 25, 2019	Neural Architecture Search	—Unverified	0
Resizable Neural Networks	Sep 25, 2019	Data AugmentationNeural Architecture Search	—Unverified	0
Scaling Up Neural Architecture Search with Big Single-Stage Models	Sep 25, 2019	Neural Architecture Search	—Unverified	0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search	Sep 25, 2019	Bayesian OptimizationNeural Architecture Search	—Unverified	0
Filter redistribution templates for iteration-lessconvolutional model reduction	Sep 25, 2019	modelNeural Architecture Search	—Unverified	0
Deep Bayesian Structure Networks	Sep 25, 2019	Bayesian InferenceNeural Architecture Search	CodeCode Available	0
VAENAS: Sampling Matters in Neural Architecture Search	Sep 25, 2019	Neural Architecture Search	—Unverified	0
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization	Sep 25, 2019	Bayesian OptimizationNeural Architecture Search	—Unverified	0
Reinforcement Learning with Chromatic Networks	Sep 25, 2019	Neural Architecture Searchreinforcement-learning	—Unverified	0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search	Sep 25, 2019	Deep Reinforcement LearningEvolutionary Algorithms	—Unverified	0
On Weight-Sharing and Bilevel Optimization in Architecture Search	Sep 25, 2019	Bilevel Optimizationfeature selection	—Unverified	0

Show:10 25 50

← PrevPage 67 of 77Next →

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