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 1076–1100 of 1915 papers

TitleStatusHype
PaRT: Parallel Learning Towards Robust and Transparent AICode0
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models—0
A Deeper Look at Zero-Cost Proxies for Lightweight NAS—0
Landscape of Neural Architecture Search across sensors: how much do they differ ?—0
UDC: Unified DNAS for Compressible TinyML Models—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural NetworksCode0
Neural Architecture Search for Inversion—0
Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search—0
Searching the Deployable Convolution Neural Networks for GPUs—0
Neural Architecture Search With Representation Mutual InformationCode0
Distribution Consistent Neural Architecture Search—0
Automatic Mixed-Precision Quantization Search of BERT—0
SPIDER: Searching Personalized Neural Architecture for Federated Learning—0
Learn Layer-wise Connections in Graph Neural Networks—0
DARTS without a Validation Set: Optimizing the Marginal Likelihood—0
Enabling NAS with Automated Super-Network Generation—0
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet—0
Learning Interpretable Models Through Multi-Objective Neural Architecture SearchCode0
M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search—0
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search—0
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition—0
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers—0
GraphPAS: Parallel Architecture Search for Graph Neural NetworksCode0
Manas: Mining Software Repositories to Assist AutoMLCode0
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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