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 851–875 of 1915 papers

TitleStatusHype
IMB-NAS: Neural Architecture Search for Imbalanced Datasets—0
Grassroots Operator Search for Model Edge Adaptation—0
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices—0
GreenFactory: Ensembling Zero-Cost Proxies to Estimate Performance of Neural Networks—0
DARTS for Inverse Problems: a Study on Stability—0
Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS—0
Binarized Neural Architecture Search for Efficient Object Recognition—0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices—0
A Study of the Learning Progress in Neural Architecture Search Techniques—0
Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation—0
ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications—0
Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks—0
ImmuNeCS: Neural Committee Search by an Artificial Immune System—0
Half Search Space is All You Need—0
Improving One-shot NAS by Suppressing the Posterior Fading—0
LANA: Latency Aware Network Acceleration—0
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference—0
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models—0
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling—0
Binarized Neural Architecture Search—0
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation—0
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms—0
Efficient Sampling for Predictor-Based Neural Architecture Search—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