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 451–500 of 1915 papers

TitleStatusHype
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
DATA: Differentiable ArchiTecture ApproximationCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
DDNAS: Discretized Differentiable Neural Architecture Search for Text ClassificationCode0
Data Aware 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
Neural Architecture Search using Property Guided SynthesisCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Automated Heterogeneous Network learning with Non-Recursive Message PassingCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural NetworkCode0
Inter-choice dependent super-network weightsCode0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
Deep Neural Architecture Search with Deep Graph Bayesian OptimizationCode0
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)Code0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Learning from Mistakes -- A Framework for Neural Architecture SearchCode0
Automated Fusion of Multimodal Electronic Health Records for Better Medical PredictionsCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
Learning to reinforcement learn for Neural Architecture SearchCode0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?Code0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover ClassificationCode0
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator SearchCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Lightweight Neural Architecture Search for Cerebral Palsy DetectionCode0
CycleGANAS: Differentiable Neural Architecture Search for CycleGANCode0
Customized Subgraph Selection and Encoding for Drug-drug Interaction PredictionCode0
CSCO: Connectivity Search of Convolutional OperatorsCode0
DetNAS: Backbone Search for Object DetectionCode0
Auto-Keras: An Efficient Neural Architecture Search SystemCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Inter-layer Transition in Neural Architecture SearchCode0
How Powerful are Performance Predictors in Neural Architecture Search?Code0
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
How to 0wn NAS in Your Spare TimeCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
Show:102550
← PrevPage 10 of 39Next →
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