SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1525 of 1915 papers

TitleStatusHype
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning—0
Trainless Model Performance Estimation for Neural Architecture Search—0
Ranking Architectures by Feature Extraction Capabilities—0
Ranking architectures using meta-learning—0
Ranking Convolutional Architectures by their Feature Extraction Capabilities—0
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale—0
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking—0
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving—0
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search—0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain—0
RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search—0
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection—0
Anomaly-resistant Graph Neural Networks via Neural Architecture Search—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Real-time Federated Evolutionary Neural Architecture Search—0
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation—0
An Introduction to Neural Architecture Search for Convolutional Networks—0
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction—0
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components—0
RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively—0
Reducing Inference Latency with Concurrent Architectures for Image Recognition—0
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering—0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
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All datasetsImageNetNAS-Bench-201, ImageNet-16-120CIFAR-10NAS-Bench-201, CIFAR-100NAS-Bench-201, CIFAR-10CIFAR-10 Image ClassificationCIFAR-100NATS-Bench Topology, CIFAR-10NATS-Bench Topology, CIFAR-100NATS-Bench Topology, ImageNet16-120Food-101NAS-Bench-101

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3—Unverified
2SPOS (FBNet-C latency)Accuracy75.1—Unverified
3SPOS (block search + channel search)Accuracy74.7—Unverified
4MUXNet-xsTop-1 Error Rate33.3—Unverified
5FBNetV2-F1Top-1 Error Rate31.7—Unverified
6LayerNAS-60MTop-1 Error Rate31—Unverified
7NASGEPTop-1 Error Rate29.51—Unverified
8MUXNet-sTop-1 Error Rate28.4—Unverified
9NN-MASS-ATop-1 Error Rate27.1—Unverified
10FBNetV2-F3Top-1 Error Rate26.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98—Unverified
2Shapley-NASAccuracy (Test)46.85—Unverified
3β-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (Test)46.71—Unverified
5ASE-NAS+Accuracy (Val)46.66—Unverified
6NARAccuracy (Test)46.66—Unverified
7BaLeNAS-TFAccuracy (Test)46.54—Unverified
8AG-NetAccuracy (Test)46.42—Unverified
9Local searchAccuracy (Test)46.38—Unverified
10NASBOTAccuracy (Test)46.37—Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55—Unverified
2GDASTop-1 Error Rate3.4—Unverified
3Bonsai-NetTop-1 Error Rate3.35—Unverified
4Net2 (2)Top-1 Error Rate3.3—Unverified
5μDARTSTop-1 Error Rate3.28—Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18—Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified