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 1851–1900 of 1915 papers

TitleStatusHype
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference CostCode0
Automatic and effective discovery of quantum kernelsCode0
RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge DevicesCode0
Design Principle Transfer in Neural Architecture Search via Large Language ModelsCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Towards modular and programmable architecture searchCode0
Auto-tuning of Deep Neural Networks by Conflicting Layer RemovalCode0
AutoST: Training-free Neural Architecture Search for Spiking TransformersCode0
Neural Architecture Search for Joint Optimization of Predictive Power and Biological KnowledgeCode0
Stage-Wise Neural Architecture SearchCode0
Sub-Architecture Ensemble Pruning in Neural Architecture SearchCode0
Neural Architecture Search For LF-MMI Trained Time Delay Neural NetworksCode0
Densely Connected Search Space for More Flexible Neural Architecture SearchCode0
ABC-Di: Approximate Bayesian Computation for Discrete DataCode0
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule NetworksCode0
Neural Architecture Search for Sentence Classification with BERTCode0
Towards NNGP-guided Neural Architecture SearchCode0
Adapting Neural Architectures Between DomainsCode0
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?Code0
Neural Architecture Search for Visual Anomaly SegmentationCode0
Fisher Task Distance and Its Application in Neural Architecture SearchCode0
Improved Automated Machine Learning from Transfer LearningCode0
AdaNet: A Scalable and Flexible Framework for Automatically Learning EnsemblesCode0
Understanding and Exploring the Network with Stochastic ArchitecturesCode0
Supernet Training for Federated Image Classification under System HeterogeneityCode0
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency AnalysisCode0
SuperSAM: Crafting a SAM Supernetwork via Structured Pruning and Unstructured Parameter PrioritizationCode0
Neural Architecture Search: Insights from 1000 PapersCode0
DeepSwarm: Optimising Convolutional Neural Networks using Swarm IntelligenceCode0
AutoSpace: Neural Architecture Search with Less Human InterferenceCode0
Deep neural network based on F-neurons and its learningCode0
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCamCode0
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture SearchCode0
Deep Neural Architecture Search with Deep Graph Bayesian OptimizationCode0
Balanced One-shot Neural Architecture OptimizationCode0
SalNAS: Efficient Saliency-prediction Neural Architecture Search with self-knowledge distillationCode0
Deeper Insights into Weight Sharing in Neural Architecture SearchCode0
W-PCA Based Gradient-Free Proxy for Efficient Search of Lightweight Language ModelsCode0
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural AcceleratorsCode0
Neural Architecture Search using Progressive EvolutionCode0
MSR-DARTS: Minimum Stable Rank of Differentiable Architecture SearchCode0
Deep Bayesian Structure NetworksCode0
Surprisingly Strong Performance Prediction with Neural Graph FeaturesCode0
Understanding Architectures Learnt by Cell-based Neural Architecture SearchCode0
Neural Architecture Search With Representation Mutual InformationCode0
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural ArchitectureCode0
Neural Architecture Search with Bayesian Optimisation and Optimal TransportCode0
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained AnalysisCode0
Towards Self-supervised and Weight-preserving Neural Architecture SearchCode0
Deep Active Learning with a Neural Architecture SearchCode0
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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