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 1601–1625 of 1915 papers

TitleStatusHype
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
A method for quantifying the generalization capabilities of generative models for solving Ising models—0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Scaling Up Neural Architecture Search with Big Single-Stage Models—0
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge—0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
DimGrow: Memory-Efficient Field-level Embedding Dimension Search—0
SCAN-Edge: Finding MobileNet-speed Hybrid Networks for Diverse Edge Devices via Hardware-Aware Evolutionary Search—0
Directed Acyclic Graph Convolutional Networks—0
Direct Federated Neural Architecture Search—0
Discovering Better Model Architectures for Medical Query Understanding—0
Discovering Multi-Hardware Mobile Models via Architecture Search—0
Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement—0
Disentangled Neural Architecture Search—0
DistDD: Distributed Data Distillation Aggregation through Gradient Matching—0
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks—0
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces—0
Distributed creation of Machine learning agents for Blockchain analysis—0
Distribution Consistent Neural Architecture Search—0
Disturbance-immune Weight Sharing for Neural Architecture Search—0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems—0
Differentiable Network Adaption with Elastic Search Space—0
Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration—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