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

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Showing 1451–1500 of 1915 papers

TitleStatusHype
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator—0
Towards Regression-Free Neural Networks for Diverse Compute Platforms—0
Architecture Augmentation for Performance Predictor Based on Graph Isomorphism—0
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches—0
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy—0
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework—0
PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search—0
Pretrained Hybrids with MAD Skills—0
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks—0
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Equivariance-aware Architectural Optimization of Neural Networks—0
Approximate Neural Architecture Search via Operation Distribution Learning—0
Probabilistic Model-Based Dynamic Architecture Search—0
Probabilistic Neural Architecture Search—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
TRACE: Tensorizing and Generalizing Supernets from Neural Architecture Search—0
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers—0
Training BatchNorm Only in Neural Architecture Search and Beyond—0
Progressive Feature Interaction Search for Deep Sparse Network—0
WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor—0
XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks—0
ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers—0
Provably Convergent Federated Trilevel Learning—0
A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism—0
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies—0
Anytime Neural Architecture Search on Tabular Data—0
Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning—0
Anti-Bandit Neural Architecture Search for Model Defense—0
Pruning Compact ConvNets for Efficient Inference—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights—0
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator—0
Across-Task Neural Architecture Search via Meta Learning—0
Pushing Joint Image Denoising and Classification to the Edge—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution—0
PV-NAS: Practical Neural Architecture Search for Video Recognition—0
Pyramid Architecture Search for Real-Time Image Deblurring—0
A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick—0
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks—0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Quantum Architecture Search with Unsupervised Representation Learning—0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization—0
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation—0
Qubit-Wise Architecture Search Method for Variational Quantum Circuits—0
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model—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