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
Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search—0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
ParZC: Parametric Zero-Cost Proxies for Efficient NAS—0
PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search—0
Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search—0
Performance-Oriented Neural Architecture Search—0
Personalized Federated Instruction Tuning via Neural Architecture Search—0
Personalized Neural Architecture Search for Federated Learning—0
Picking up the pieces: separately evaluating supernet training and architecture selection—0
Poisoning the Search Space in Neural Architecture Search—0
Poisson Process for Bayesian Optimization—0
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference—0
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator—0
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy—0
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework—0
PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search—0
Pretrained Hybrids with MAD Skills—0
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud—0
Probabilistic Model-Based Dynamic Architecture Search—0
Probabilistic Neural Architecture Search—0
Progressive Feature Interaction Search for Deep Sparse Network—0
Provably Convergent Federated Trilevel Learning—0
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies—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