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 751–775 of 1915 papers

TitleStatusHype
GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices—0
FedorAS: Federated Architecture Search under system heterogeneity—0
End-to-end Keyword Spotting using Neural Architecture Search and Quantization—0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI—0
FGNAS: FPGA-Aware Graph Neural Architecture Search—0
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction—0
Filter redistribution templates for iteration-lessconvolutional model reduction—0
Finding Competitive Network Architectures Within a Day Using UCT—0
Accelerate Intermittent Deep Inference—0
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition—0
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—0
Fine-Grained Neural Architecture Search—0
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation—0
Fine-Tuning DARTS for Image Classification—0
A Survey of Techniques for Optimizing Transformer Inference—0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems—0
Enabling NAS with Automated Super-Network Generation—0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
FLASH: Fast Neural Architecture Search with Hardware Optimization—0
Compatibility-aware Heterogeneous Visual Search—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Flexible Channel Dimensions for Differentiable Architecture Search—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Show:102550
← PrevPage 31 of 77Next →
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