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

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Showing 1226–1250 of 1915 papers

TitleStatusHype
SPIDER: Searching Personalized Neural Architecture for Federated Learning—0
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Autonomous Agents—0
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems—0
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks—0
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation—0
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search—0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning—0
Straight Through Gumbel Softmax Estimator based Bimodal Neural Architecture Search for Audio-Visual Deepfake Detection—0
StressNAS: Affect State and Stress Detection Using Neural Architecture Search—0
Structural Analysis of Sparse Neural Networks—0
Structure of Artificial Neural Networks -- Empirical Investigations—0
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks—0
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
Superkernel Neural Architecture Search for Image Denoising—0
A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation—0
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification—0
SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design—0
Symbolic Regression on FPGAs for Fast Machine Learning Inference—0
Syno: Structured Synthesis for Neural Operators—0
Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion—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