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 801–850 of 1915 papers

TitleStatusHype
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—0
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation—0
A Survey of Techniques for Optimizing Transformer Inference—0
Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm—0
Generalization Guarantees for Neural Architecture Search with Train-Validation Split—0
Generalization Properties of NAS under Activation and Skip Connection Search—0
Enabling NAS with Automated Super-Network Generation—0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
A Surgery of the Neural Architecture Evaluators—0
Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator—0
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures—0
Across-Task Neural Architecture Search via Meta Learning—0
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition—0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
Hyperparameter Optimization in Machine Learning—0
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization—0
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth—0
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search—0
Landscape of Neural Architecture Search across sensors: how much do they differ ?—0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
Evaluating Efficient Performance Estimators of Neural Architectures—0
Global optimization of graph acquisition functions for neural architecture search—0
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition—0
GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning—0
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring—0
A Study on the Intersection of GPU Utilization and CNN Inference—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
HQNAS: Auto CNN deployment framework for joint quantization and architecture search—0
GP-NAS-ensemble: a model for NAS Performance Prediction—0
GP-NAS: Gaussian Process Based Neural Architecture Search—0
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search—0
GPUNet: Searching the Deployable Convolution Neural Networks for GPUs—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Gradient-free Policy Architecture Search and Adaptation—0
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning—0
Accelerator-aware Neural Network Design using AutoML—0
Graph Differentiable Architecture Search with Structure Learning—0
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search—0
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness—0
Graph is all you need? Lightweight data-agnostic neural architecture search without training—0
Graph Neural Architecture Search with GPT-4—0
Graph Neural Network Architecture Search for Molecular Property Prediction—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Graph Neural Networks Are Evolutionary Algorithms—0
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models—0
Binarized Neural Architecture Search for Efficient Object 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