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 14511475 of 1915 papers

TitleStatusHype
LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning0
Neighbourhood Distillation: On the benefits of non end-to-end distillation0
Effective Regularization Through Loss-Function Metalearning0
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture SearchCode0
A Surgery of the Neural Architecture Evaluators0
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search0
Once Quantized for All: Progressively Searching for Quantized Compact Models0
Disentangled Neural Architecture Search0
Multi-Pass Transformer for Machine Translation0
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models0
Evolutionary Architecture Search for Graph Neural NetworksCode0
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution0
MSR-DARTS: Minimum Stable Rank of Differentiable Architecture SearchCode0
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search0
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation0
AutoML for Multilayer Perceptron and FPGA Co-design0
DANCE: Differentiable Accelerator/Network Co-Exploration0
Binarized Neural Architecture Search for Efficient Object Recognition0
AutoKWS: Keyword Spotting with Differentiable Architecture Search0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS0
Boosting Share Routing for Multi-task Learning0
Neural Architecture Search For Keyword Spotting0
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms0
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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