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 501–550 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
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—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
Towards Optimal Compression: Joint Pruning and Quantization—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
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
Neural Architecture Search via Two Constant Shared Weights InitialisationsCode0
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge TransferCode0
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
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on GradientsCode1
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the EdgeCode1
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost ProxiesCode1
GP-NAS-ensemble: a model for NAS Performance Prediction—0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
Efficient Training Under Limited ResourcesCode0
Neural Architecture Search: Insights from 1000 PapersCode0
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation—0
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search—0
DQNAS: Neural Architecture Search using Reinforcement Learning—0
β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture SearchCode1
Efficient Evaluation Methods for Neural Architecture Search: A Survey—0
Adaptive Neural Networks Using Residual Fitting—0
Pruning Compact ConvNets for Efficient Inference—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
Extensible and Efficient Proxy for Neural Architecture Search—0
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching—0
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NASCode0
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search—0
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different DatasetsCode0
Pseudo-Inverted Bottleneck Convolution for DARTS Search SpaceCode0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
DAS: Neural Architecture Search via Distinguishing Activation Score—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified