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
Can GPT-4 Perform Neural Architecture Search?Code1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Accelerating Neural Architecture Search via Proxy DataCode1
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture SearchCode1
Canvas: End-to-End Kernel Architecture Search in Neural NetworksCode1
Efficient Architecture Search for Diverse TasksCode1
Discovering Neural WiringsCode1
Efficient Forward Architecture SearchCode1
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-IdentificationCode1
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed SpacesCode1
EfficientTDNN: Efficient Architecture Search for Speaker RecognitionCode1
EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture SearchCode1
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile DevicesCode1
Searching a Compact Architecture for Robust Multi-Exposure Image FusionCode1
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT DevicesCode1
EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture SearchCode1
BN-NAS: Neural Architecture Search with Batch NormalizationCode1
Evolutionary Neural Architecture Search for Transformer in Knowledge TracingCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Angle-based Search Space Shrinking for Neural Architecture SearchCode1
Extensible Proxy for Efficient NASCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
FEAR: A Simple Lightweight Method to Rank ArchitecturesCode1
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture SearchCode1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum SearchCode1
Firefly Neural Architecture Descent: a General Approach for Growing Neural NetworksCode1
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image SegmentationCode1
β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture SearchCode1
Block-Wisely Supervised Neural Architecture Search With Knowledge DistillationCode1
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network QuantizationCode1
ColdNAS: Search to Modulate for User Cold-Start RecommendationCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningCode1
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free CompressionCode1
AutoML: A Survey of the State-of-the-ArtCode1
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic ClassificationCode1
AutoSNN: Towards Energy-Efficient Spiking Neural NetworksCode1
Hierarchical quantum circuit representations for neural architecture searchCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
Are Labels Necessary for Neural Architecture Search?Code1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
Blockwisely Supervised Neural Architecture Search with Knowledge DistillationCode1
BM-NAS: Bilevel Multimodal Neural Architecture SearchCode1
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture SearchCode1
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine TranslationCode1
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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