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 101–150 of 1915 papers

TitleStatusHype
Automated Search for Resource-Efficient Branched Multi-Task NetworksCode1
Dataset Condensation with Distribution MatchingCode1
Accelerating Neural Architecture Search via Proxy DataCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture SearchCode1
einspace: Searching for Neural Architectures from Fundamental OperationsCode1
Discovering Neural WiringsCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
Blockwisely Supervised Neural Architecture Search with Knowledge DistillationCode1
Dataset Condensation with Gradient MatchingCode1
Evolutionary Neural AutoML for Deep LearningCode1
Evolutionary Neural Cascade Search across SupernetworksCode1
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
Exploring Relational Context for Multi-Task Dense PredictionCode1
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image SegmentationCode1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum SearchCode1
Angle-based Search Space Shrinking for Neural Architecture SearchCode1
BM-NAS: Bilevel Multimodal Neural Architecture SearchCode1
BN-NAS: Neural Architecture Search with Batch NormalizationCode1
DataDAM: Efficient Dataset Distillation with Attention MatchingCode1
Few-shot Neural Architecture SearchCode1
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture SearchCode1
Firefly Neural Architecture Descent: a General Approach for Growing Neural NetworksCode1
Automated Concatenation of Embeddings for Structured PredictionCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
DC-BENCH: Dataset Condensation BenchmarkCode1
Demystifying Map Space Exploration for NPUsCode1
Cyclic Differentiable Architecture SearchCode1
AutoGL: A Library for Automated Graph LearningCode1
AutoKE: An automatic knowledge embedding framework for scientific machine learningCode1
Contrastive Neural Architecture Search with Neural Architecture ComparatorsCode1
Contrastive Embeddings for Neural ArchitecturesCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
Hierarchical quantum circuit representations for neural architecture searchCode1
DARTS: Differentiable Architecture SearchCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
Are Labels Necessary for Neural Architecture Search?Code1
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
Automatic Relation-aware Graph Network ProliferationCode1
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and DirectionsCode1
Automated Machine Learning on Graphs: A SurveyCode1
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT ScansCode1
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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (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
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