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 1051–1100 of 1915 papers

TitleStatusHype
Neural Architecture Search using Progressive EvolutionCode0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring—0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture SearchCode0
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting—0
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms—0
A Hardware-Aware System for Accelerating Deep Neural Network Optimization—0
SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices—0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Neural Architecture Search for Dense Prediction Tasks in Computer Vision—0
Neural Architecture Search for Energy Efficient Always-on Audio Models—0
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture SearchCode0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy—0
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models—0
Self Semi Supervised Neural Architecture Search for Semantic Segmentation—0
DropNAS: Grouped Operation Dropout for Differentiable Architecture SearchCode0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
Unifying and Boosting Gradient-Based Training-Free Neural Architecture SearchCode0
Neural Architecture Searching for Facial Attributes-based Depression Recognition—0
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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified