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 401–450 of 1915 papers

TitleStatusHype
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
Inter-choice dependent super-network weightsCode0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Inter-layer Transition in Neural Architecture SearchCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Inner Ensemble Networks: Average Ensemble as an Effective RegularizerCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane DetectionCode0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
On Adversarial Robustness: A Neural Architecture Search perspectiveCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Automating Data Science Pipelines with Tensor CompletionCode0
Automated Dominative Subspace Mining for Efficient Neural Architecture SearchCode0
A Data-driven Approach to Neural Architecture Search InitializationCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
DCLP: Neural Architecture Predictor with Curriculum Contrastive LearningCode0
Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NASCode0
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
InstaNAS: Instance-aware Neural Architecture SearchCode0
Katib: A Distributed General AutoML Platform on KubernetesCode0
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture SearchCode0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
How to 0wn the NAS in Your Spare TimeCode0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture SearchCode0
How Powerful are Performance Predictors in Neural Architecture Search?Code0
How to 0wn NAS in Your Spare TimeCode0
DATA: Differentiable ArchiTecture ApproximationCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
Data Aware Neural Architecture SearchCode0
Neural Architecture Search using Property Guided SynthesisCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
Automatic Generation of Neural Architecture Search SpacesCode0
Automated Heterogeneous Network learning with Non-Recursive Message PassingCode0
Hierarchical Representations for Efficient Architecture SearchCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
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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