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

Showing 17011750 of 1915 papers

TitleStatusHype
Siamese-NAS: Using Trained Samples Efficiently to Find Lightweight Neural Architecture by Prior KnowledgeCode0
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture SearchCode0
Simple And Efficient Architecture Search for Convolutional Neural NetworksCode0
MoGA: Searching Beyond MobileNetV3Code0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problemsCode0
Zero-Cost Operation Scoring in Differentiable Architecture SearchCode0
Simultaneous Weight and Architecture Optimization for Neural NetworksCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Evolutionary Architecture Search for Graph Neural NetworksCode0
Event Classification with Multi-step Machine LearningCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Evaluating the Search Phase of Neural Architecture SearchCode0
MRF-UNets: Searching UNet with Markov Random FieldsCode0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
YOLOBench: Benchmarking Efficient Object Detectors on Embedded SystemsCode0
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture SearchCode0
Single-DARTS: Towards Stable Architecture SearchCode0
Encodings for Prediction-based Neural Architecture SearchCode0
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter OptimizationCode0
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 HoursCode0
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural NetworksCode0
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different DatasetsCode0
EmProx: Neural Network Performance Estimation For Neural Architecture SearchCode0
Multi-fidelity Neural Architecture Search with Knowledge DistillationCode0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
Multinomial Distribution Learning for Effective Neural Architecture SearchCode0
Élivágar: Efficient Quantum Circuit Search for ClassificationCode0
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen DatasetsCode0
Multi-Objective Evolutionary Neural Architecture Search for Recurrent Neural NetworksCode0
Random Search and Reproducibility for Neural Architecture SearchCode0
To Share or Not To Share: A Comprehensive Appraisal of Weight-SharingCode0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
Small-Group Learning, with Application to Neural Architecture SearchCode0
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge TransferCode0
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive RecommendationCode0
SMASH: One-Shot Model Architecture Search through HyperNetworksCode0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
AutoDispNet: Improving Disparity Estimation With AutoMLCode0
Efficient Training Under Limited ResourcesCode0
Auto deep learning for bioacoustic signalsCode0
Benchmarking Deep Spiking Neural Networks on Neuromorphic HardwareCode0
Multi-Objective Reinforced Evolution in Mobile Neural Architecture SearchCode0
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial AttacksCode0
SOTERIA: In Search of Efficient Neural Networks for Private InferenceCode0
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image SegmentationCode0
Variational Depth Search in ResNetsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified