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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 251–300 of 1915 papers

TitleStatusHype
deepstruct -- linking deep learning and graph theoryCode1
Contrastive Embeddings for Neural ArchitecturesCode1
AutoSNN: Towards Energy-Efficient Spiking Neural NetworksCode1
Contrastive Neural Architecture Search with Neural Architecture ComparatorsCode1
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture SearchCode1
Differential Evolution for Neural Architecture SearchCode1
Dataset Condensation with Distribution MatchingCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Dataset Condensation with Gradient MatchingCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
DARTS: Differentiable Architecture SearchCode1
DataDAM: Efficient Dataset Distillation with Attention MatchingCode1
AOWS: Adaptive and optimal network width search with latency constraintsCode1
Deep Multimodal Neural Architecture SearchCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter OptimizationCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion PerspectiveCode1
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
DC-BENCH: Dataset Condensation BenchmarkCode1
Discretization-Aware Architecture SearchCode1
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?Code1
DrNAS: Dirichlet Neural Architecture SearchCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Block-Wisely Supervised Neural Architecture Search With Knowledge DistillationCode1
EEEA-Net: An Early Exit Evolutionary Neural Architecture SearchCode1
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-IdentificationCode1
Efficient Forward Architecture SearchCode1
Cyclic Differentiable Architecture SearchCode1
BN-NAS: Neural Architecture Search with Batch NormalizationCode1
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture SearchCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
EfficientPose: Efficient Human Pose Estimation with Neural Architecture SearchCode1
EfficientTDNN: Efficient Architecture Search for Speaker RecognitionCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Are Labels Necessary for Neural Architecture Search?Code1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
β-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
b-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture SearchCode1
Evolutionary Neural AutoML for Deep LearningCode1
Evolutionary Neural Cascade Search across SupernetworksCode1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum SearchCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
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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