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 776800 of 1915 papers

TitleStatusHype
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography0
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation0
Enabling NAS with Automated Super-Network Generation0
FNAS: Uncertainty-Aware Fast Neural Architecture Search0
FocusFormer: Focusing on What We Need via Architecture Sampler0
Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications0
A Surgery of the Neural Architecture Evaluators0
From Hand-Crafted Metrics to Evolved Training-Free Performance Predictors for Neural Architecture Search via Genetic Programming0
Across-Task Neural Architecture Search via Meta Learning0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification0
Fruit Classification System with Deep Learning and Neural Architecture Search0
FSD: Fully-Specialized Detector via Neural Architecture Search0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary0
FTSO: Effective NAS via First Topology Second Operator0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection0
Fast Hardware-Aware Neural Architecture Search0
Full Stack Optimization of Transformer Inference: a Survey0
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search0
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images0
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm0
HNAS-reg: hierarchical neural architecture search for deformable medical image registration0
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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