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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 501–550 of 1915 papers

TitleStatusHype
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural NetworksCode0
IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate ImportanceCode0
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural ArchitectureCode0
BINAS: Bilinear Interpretable Neural Architecture SearchCode0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
AutoSpace: Neural Architecture Search with Less Human InterferenceCode0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
Adaptive hybrid activation function for deep neural networksCode0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
Inner Ensemble Networks: Average Ensemble as an Effective RegularizerCode0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
Inter-layer Transition in Neural Architecture SearchCode0
Knowledge-aware Evolutionary Graph Neural Architecture SearchCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
AutoDispNet: Improving Disparity Estimation With AutoMLCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Auto deep learning for bioacoustic signalsCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
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
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
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
Adapting Neural Architectures Between DomainsCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
How to 0wn NAS in Your Spare TimeCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
How to 0wn the NAS in Your Spare TimeCode0
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture SearchCode0
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge NodesCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
AdaNet: A Scalable and Flexible Framework for Automatically Learning EnsemblesCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter TuningCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
Cascade Bagging for Accuracy Prediction with Few Training SamplesCode0
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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