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 301–350 of 1915 papers

TitleStatusHype
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object DetectionCode1
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
Evolutionary Computation in the Era of Large Language Model: Survey and RoadmapCode2
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
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts—0
Efficient Architecture Search via Bi-level Data Pruning—0
SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search—0
IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate ImportanceCode0
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion ModelsCode2
Provably Convergent Federated Trilevel Learning—0
Weight-Entanglement Meets Gradient-Based Neural Architecture Search—0
OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing OperatorsCode1
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
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
Combined Scheduling, Memory Allocation and Tensor Replacement for Minimizing Off-Chip Data Accesses of DNN Accelerators—0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks—0
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
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale—0
AutoML for Large Capacity Modeling of Meta's Ranking Systems—0
CycleGANAS: Differentiable Neural Architecture Search for CycleGANCode0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search—0
Auto deep learning for bioacoustic signalsCode0
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric—0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
Show:102550
← PrevPage 7 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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified