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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 1915 papers

TitleStatusHype
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural NetworksCode0
Inter-layer Transition in Neural Architecture SearchCode0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
BINAS: Bilinear Interpretable Neural Architecture SearchCode0
Continual and Multi-Task Architecture SearchCode0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
CONetV2: Efficient Auto-Channel Size Optimization for CNNsCode0
AutoSpace: Neural Architecture Search with Less Human InterferenceCode0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly EasyCode0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
CONet: Channel Optimization for Convolutional Neural NetworksCode0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Inner Ensemble Networks: Average Ensemble as an Effective RegularizerCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
Adaptive hybrid activation function for deep neural networksCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
Inter-choice dependent super-network weightsCode0
Knowledge-aware Evolutionary Graph Neural Architecture SearchCode0
NAS evaluation is frustratingly hardCode0
AutoDispNet: Improving Disparity Estimation With AutoMLCode0
Auto deep learning for bioacoustic signalsCode0
Combinatorial Bayesian Optimization using the Graph Cartesian ProductCode0
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image SegmentationCode0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razorCode0
How to 0wn the NAS in Your Spare TimeCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
How Powerful are Performance Predictors in Neural Architecture Search?Code0
Adapting Neural Architectures Between DomainsCode0
Hierarchical Representations for Efficient Architecture SearchCode0
CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NASCode0
How to 0wn NAS in Your Spare TimeCode0
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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