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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 of 1915 papers

TitleStatusHype
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
Automated Heterogeneous Network learning with Non-Recursive Message PassingCode0
BINAS: Bilinear Interpretable Neural Architecture SearchCode0
DDNAS: Discretized Differentiable Neural Architecture Search for Text ClassificationCode0
Automated Dominative Subspace Mining for Efficient Neural Architecture SearchCode0
Deep Active Learning with a Neural Architecture SearchCode0
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained AnalysisCode0
Deep Bayesian Structure NetworksCode0
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture SearchCode0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
Deeper Insights into Weight Sharing in Neural Architecture SearchCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
Automated Fusion of Multimodal Electronic Health Records for Better Medical PredictionsCode0
Inter-layer Transition in Neural Architecture SearchCode0
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose PredictionCode0
Inner Ensemble Networks: Average Ensemble as an Effective RegularizerCode0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover ClassificationCode0
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator SearchCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
CycleGANAS: Differentiable Neural Architecture Search for CycleGANCode0
Customized Subgraph Selection and Encoding for Drug-drug Interaction PredictionCode0
Long-term Reproducibility for Neural Architecture SearchCode0
CSCO: Connectivity Search of Convolutional OperatorsCode0
Auto-Keras: An Efficient Neural Architecture Search SystemCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural NetworksCode0
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture SearchCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
DetNAS: Backbone Search for Object DetectionCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Contrastive Self-supervised Neural Architecture SearchCode0
AutoGrow: Automatic Layer Growing in Deep Convolutional NetworksCode0
DFG-NAS: Deep and Flexible Graph Neural Architecture SearchCode0
Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture SearchCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
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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