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 1651–1675 of 1915 papers

TitleStatusHype
EDAS: Efficient and Differentiable Architecture Search—0
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions—0
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey—0
Edge-featured Graph Neural Architecture Search—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Effective, Efficient and Robust Neural Architecture Search—0
Effective Regularization Through Loss-Function Metalearning—0
ALT: An Automatic System for Long Tail Scenario Modeling—0
Searching for Controllable Image Restoration Networks—0
Searching for Convolutions and a More Ambitious NAS—0
Efficient Architecture Search for Continual Learning—0
Differentiable architecture search with multi-dimensional attention for spiking neural networks—0
Efficient Architecture Search via Bi-level Data Pruning—0
Differentiable Architecture Search with Random Features—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search—0
Differentiable Architecture Search with Ensemble Gumbel-Softmax—0
All in One Bad Weather Removal Using Architectural Search—0
Efficient Deep Neural Networks—0
Efficient Differentiable Neural Architecture Search with Meta Kernels—0
Efficient Differentiable Neural Architecture Search with Model Parallelism—0
Efficient Evaluation Methods for Neural Architecture Search: A Survey—0
Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs—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