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 1151–1200 of 1915 papers

TitleStatusHype
Across-Task Neural Architecture Search via Meta Learning—0
On the Security Risks of AutoMLCode0
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
ZARTS: On Zero-order Optimization for Neural Architecture Search—0
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
An Analysis of Super-Net Heuristics in Weight-Sharing NAS—0
Max and Coincidence Neurons in Neural Networks—0
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problemsCode0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Personalized Neural Architecture Search for Federated Learning—0
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search—0
ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators—0
Picking up the pieces: separately evaluating supernet training and architecture selection—0
Ranking Convolutional Architectures by their Feature Extraction Capabilities—0
What to expect of hardware metric predictors in NAS—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
Hardware-Aware Network Transformation—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
Multi-objective Optimization by Learning Space Partition—0
Multi-objective optimization for Hardware-aware Neural Architecture Search—0
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search—0
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture SearchCode0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Self-Supervised Neural Architecture Search for Imbalanced DatasetsCode0
Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach—0
Neural Architecture Search in operational context: a remote sensing case-study—0
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking—0
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search—0
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance—0
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search—0
Neural Ensemble Search via Bayesian Sampling—0
Automated Robustness with Adversarial Training as a Post-Processing Step—0
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
Edge-featured Graph Neural Architecture Search—0
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization—0
Searching for Two-Stream Models in Multivariate Space for Video Recognition—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