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 651–700 of 1915 papers

TitleStatusHype
Personalized Federated Instruction Tuning via Neural Architecture Search—0
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones—0
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales—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
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection—0
ParZC: Parametric Zero-Cost Proxies for Efficient NAS—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
Élivágar: Efficient Quantum Circuit Search for ClassificationCode0
SeqNAS: Neural Architecture Search for Event Sequence ClassificationCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts—0
Efficient Architecture Search via Bi-level Data Pruning—0
IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate ImportanceCode0
Provably Convergent Federated Trilevel Learning—0
SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search—0
Weight-Entanglement Meets Gradient-Based Neural Architecture Search—0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Combined Scheduling, Memory Allocation and Tensor Replacement for Minimizing Off-Chip Data Accesses of DNN Accelerators—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks—0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback—0
SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape—0
Masked Autoencoders Are Robust Neural Architecture Search Learners—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search—0
AutoML for Large Capacity Modeling of Meta's Ranking Systems—0
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale—0
CycleGANAS: Differentiable Neural Architecture Search for CycleGANCode0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Show:102550
← PrevPage 14 of 39Next →
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