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

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Showing 601–650 of 1915 papers

TitleStatusHype
Towards Accurate and Robust Architectures via Neural Architecture Search—0
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search—0
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference CostCode0
A method for quantifying the generalization capabilities of generative models for solving Ising models—0
A Lightweight Neural Architecture Search Model for Medical Image Classification—0
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
Structural Pruning of Pre-trained Language Models via Neural Architecture SearchCode0
ATOM: Attention Mixer for Efficient Dataset DistillationCode0
Graph is all you need? Lightweight data-agnostic neural architecture search without training—0
CSCO: Connectivity Search of Convolutional OperatorsCode0
Surprisingly Strong Performance Prediction with Neural Graph FeaturesCode0
Efficient NAS with FaDE on Hierarchical Spaces—0
Identifying phase transitions in physical systems with neural networks: a neural architecture search perspective—0
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover MappingCode0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
Network architecture search of X-ray based scientific applications—0
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NASCode0
ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers—0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search—0
Mixed-precision Supernet Training from Vision Foundation Models using Low Rank Adapter—0
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations—0
Neural Architecture Search for Sentence Classification with BERTCode0
On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network FrequencyCode0
Building Optimal Neural Architectures using Interpretable KnowledgeCode0
Robust NAS under adversarial training: benchmark, theory, and beyond—0
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning ApproachCode0
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction—0
Multi-Objective Evolutionary Neural Architecture Search for Recurrent Neural NetworksCode0
Chain-structured neural architecture search for financial time series forecasting—0
Efficient Multiplayer Battle Game Optimizer for Adversarial Robust Neural Architecture SearchCode0
Anytime Neural Architecture Search on Tabular Data—0
SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different LanguagesCode0
Multiple Population Alternate Evolution Neural Architecture Search—0
ECToNAS: Evolutionary Cross-Topology Neural Architecture SearchCode0
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision—0
Qubit-Wise Architecture Search Method for Variational Quantum Circuits—0
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NASCode0
Neural Architecture Search using Particle Swarm and Ant Colony Optimization—0
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth—0
Revisiting Learning-based Video Motion Magnification for Real-time Processing—0
NASH: Neural Architecture Search for Hardware-Optimized Machine Learning ModelsCode0
Encodings for Prediction-based Neural Architecture SearchCode0
Parallel Hyperparameter Optimization Of Spiking Neural NetworkCode0
FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution RobustnessCode0
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs—0
Adaptive quantization with mixed-precision based on low-cost proxy—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