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

Showing 526550 of 1915 papers

TitleStatusHype
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
Show:102550
← PrevPage 22 of 77Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified