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 1251–1300 of 1915 papers

TitleStatusHype
Automated Robustness with Adversarial Training as a Post-Processing Step—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems—0
TensorSocket: Shared Data Loading for Deep Learning Training—0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Neighborhood-Aware Neural Architecture Search—0
Neighbourhood Distillation: On the benefits of non end-to-end distillation—0
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization—0
Network Architecture Search for Domain Adaptation—0
Network architecture search of X-ray based scientific applications—0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
AutoKWS: Keyword Spotting with Differentiable Architecture Search—0
Network Space Search for Pareto-Efficient Spaces—0
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
TextNAS: A Neural Architecture Search Space tailored for Text Representation—0
Neural Architecture Design and Robustness: A Dataset—0
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family—0
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Neural Architecture Optimization with Graph VAE—0
Neural Architecture Performance Prediction Using Graph Neural Networks—0
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
Auto-GNN: Neural Architecture Search of Graph Neural Networks—0
Neural Architecture Search as Sparse Supernet—0
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search—0
Neural Architecture Search based Global-local Vision Mamba for Palm-Vein Recognition—0
Neural Architecture Search based on Cartesian Genetic Programming Coding Method—0
Neural Architecture Search by Learning a Hierarchical Search Space—0
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search—0
Neural Architecture Search for Class-incremental Learning—0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification—0
Neural Architecture Search for Deep Face Recognition—0
Neural Architectural Backdoors—0
Neural Architecture Search for Dense Prediction Tasks in Computer Vision—0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo—0
Neural Architecture Search for Energy Efficient Always-on Audio Models—0
Neural Architecture Search For Fault Diagnosis—0
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System—0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing—0
Neural Architecture Search for Intel Movidius VPU—0
Neural Architecture Search for Inversion—0
The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol—0
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models—0
Neural Architecture Search For Keyword Spotting—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