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 1176–1200 of 1915 papers

TitleStatusHype
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient—0
Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion—0
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization—0
Multi-Objective Neural Architecture Search for In-Memory Computing—0
Multi-Objective Neural Architecture Search by Learning Search Space Partitions—0
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search—0
Multi-objective Optimization by Learning Space Partition—0
Multi-objective optimization for Hardware-aware Neural Architecture Search—0
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation—0
Multi-Pass Transformer for Machine Translation—0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Multiple Population Alternate Evolution Neural Architecture Search—0
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
Warm-starting DARTS using meta-learning—0
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks—0
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach—0
Multi-Task Neural Architecture Search Using Architecture Embedding and Transfer Rank—0
Mutation is all you need—0
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models—0
TAPAS: Train-less Accuracy Predictor for Architecture Search—0
WAS-VTON: Warping Architecture Search for Virtual Try-on Network—0
NADER: Neural Architecture Design via Multi-Agent Collaboration—0
NAHAS: Neural Architecture and Hardware Accelerator Search—0
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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-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (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
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified