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 651–675 of 1915 papers

TitleStatusHype
Personalized Federated Instruction Tuning via Neural Architecture Search—0
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales—0
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification—0
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search—0
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware—0
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Autonomous Agents—0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models—0
Designing deep neural networks for driver intention recognition—0
Poisson Process for Bayesian Optimization—0
ParZC: Parametric Zero-Cost Proxies for Efficient NAS—0
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection—0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays—0
DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems—0
Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization—0
Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron—0
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural NetworksCode0
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge—0
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search—0
Quantum Architecture Search with Unsupervised Representation Learning—0
Automated Fusion of Multimodal Electronic Health Records for Better Medical PredictionsCode0
MicroNAS: Zero-Shot Neural Architecture Search for MCUs—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