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 151–175 of 1915 papers

TitleStatusHype
TensorSocket: Shared Data Loading for Deep Learning Training—0
A Survey on Neural Architecture Search Based on Reinforcement Learning—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting—0
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition—0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
OStr-DARTS: Differentiable Neural Architecture Search based on Operation StrengthCode0
Pushing Joint Image Denoising and Classification to the Edge—0
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen DatasetsCode0
Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods—0
NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid ModelsCode0
MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement—0
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables—0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification—0
MoRe Fine-Tuning with 10x Fewer ParametersCode1
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator—0
TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series ClassificationCode1
NAS-BNN: Neural Architecture Search for Binary Neural NetworksCode1
SCAN-Edge: Finding MobileNet-speed Hybrid Networks for Diverse Edge Devices via Hardware-Aware Evolutionary Search—0
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices—0
NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation—0
Design Principle Transfer in Neural Architecture Search via Large Language ModelsCode0
NEAR: A Training-Free Pre-Estimator of Machine Learning Model PerformanceCode0
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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