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 801–850 of 1915 papers

TitleStatusHype
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference CostCode0
Hierarchical Representations for Efficient Architecture SearchCode0
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image SegmentationCode0
A Classification of G-invariant Shallow Neural NetworksCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Generalized Latency Performance Estimation for Once-For-All Neural Architecture SearchCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Auto-tuning of Deep Neural Networks by Conflicting Layer RemovalCode0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Self-supervised Representation Learning for Evolutionary Neural Architecture SearchCode0
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training DataCode0
Differentially-private Federated Neural Architecture SearchCode0
Hardware Aware Neural Network Architectures using FbNetCode0
Efficient Neural Architecture Search via Proximal IterationsCode0
Differentiable Neural Architecture Search in Equivalent Space with Exploration EnhancementCode0
EENA: Efficient Evolution of Neural ArchitectureCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Gibbs randomness-compression proposition: An efficient deep learningCode0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
ShiftNAS: Improving One-shot NAS via Probability ShiftCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
Language Models with TransformersCode0
Guided Evolution for Neural Architecture SearchCode0
AutoST: Training-free Neural Architecture Search for Spiking TransformersCode0
Autoequivariant Network Search via Group DecompositionCode0
Simultaneous Weight and Architecture Optimization for Neural NetworksCode0
Differentiable NAS Framework and Application to Ads CTR PredictionCode0
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NASCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Latency-Aware Differentiable Neural Architecture SearchCode0
Reinforced Evolutionary Neural Architecture SearchCode0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
Differentiable Graph Optimization for Neural Architecture Search—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Differentiable architecture search with multi-dimensional attention for spiking neural networks—0
Differentiable Architecture Search with Random Features—0
Differentiable Architecture Search with Ensemble Gumbel-Softmax—0
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
DICE: Deep Significance Clustering for Outcome-Aware Stratification—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators—0
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)—0
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search—0
Anomaly-resistant Graph Neural Networks via Neural Architecture Search—0
An Introduction to Neural Architecture Search for Convolutional Networks—0
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm—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