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 501–525 of 1915 papers

TitleStatusHype
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—0
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Search to Capture Long-range Dependency with Stacking GNNs for Graph ClassificationCode0
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Towards Optimal Compression: Joint Pruning and Quantization—0
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia—0
Operation-level Progressive Differentiable Architecture SearchCode0
Improving Differentiable Architecture Search via Self-Distillation—0
Unified Functional Hashing in Automatic Machine LearningCode1
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge TransferCode0
Neural Architecture Search via Two Constant Shared Weights InitialisationsCode0
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"Code0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese NetworksCode0
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningCode1
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—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