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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 101–150 of 1915 papers

TitleStatusHype
Automated Machine Learning on Graphs: A SurveyCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Accelerating Neural Architecture Search via Proxy DataCode1
EfficientPose: Efficient Human Pose Estimation with Neural Architecture SearchCode1
EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture SearchCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Discovering Neural WiringsCode1
Searching a Compact Architecture for Robust Multi-Exposure Image FusionCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture SearchCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
BM-NAS: Bilevel Multimodal Neural Architecture SearchCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Evolutionary Neural AutoML for Deep LearningCode1
b-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
β-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture SearchCode1
Exploring Relational Context for Multi-Task Dense PredictionCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
Angle-based Search Space Shrinking for Neural Architecture SearchCode1
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel DimensionsCode1
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
DARTS: Differentiable Architecture SearchCode1
Few-shot Neural Architecture SearchCode1
Block-Wisely Supervised Neural Architecture Search With Knowledge DistillationCode1
Firefly Neural Architecture Descent: a General Approach for Growing Neural NetworksCode1
AutoKE: An automatic knowledge embedding framework for scientific machine learningCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
Dataset Condensation with Distribution MatchingCode1
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter OptimizationCode1
Contrastive Neural Architecture Search with Neural Architecture ComparatorsCode1
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
ColdNAS: Search to Modulate for User Cold-Start RecommendationCode1
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-IdentificationCode1
ChamNet: Towards Efficient Network Design through Platform-Aware Model AdaptationCode1
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
Hierarchical quantum circuit representations for neural architecture searchCode1
AutoGL: A Library for Automated Graph LearningCode1
Contrastive Embeddings for Neural ArchitecturesCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
Are Labels Necessary for Neural Architecture Search?Code1
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT ScansCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
Automated Concatenation of Embeddings for Structured PredictionCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and DirectionsCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
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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