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 1001–1050 of 1915 papers

TitleStatusHype
Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS—0
Neural Architecture Search as Sparse Supernet—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
Neural Architecture Search for Deep Face Recognition—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
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
Neural Architecture Search For Keyword Spotting—0
Neural Architecture Search for Natural Language Understanding—0
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
Neural Architecture Search for Speech Emotion Recognition—0
Neural Architecture Search in a Proxy Validation Loss Landscape—0
Neural Architecture Search in Embedding Space—0
Neural Architecture Searching for Facial Attributes-based Depression Recognition—0
Neural Architecture Search in operational context: a remote sensing case-study—0
Neural Architecture Search of Hybrid Models for NPU-CIM Heterogeneous AR/VR Devices—0
Neural Architecture Search on Acoustic Scene Classification—0
Neural Architecture Search on Efficient Transformers and Beyond—0
Neural Architecture Search Over a Graph Search Space—0
Federated Neural Architecture Search—0
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy—0
Neural Architecture Search using Particle Swarm and Ant Colony Optimization—0
Neural Architecture Search via Combinatorial Multi-Armed Bandit—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework—0
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia—0
Hyperparameter optimization with REINFORCE and Transformers—0
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search—0
NeuralArTS: Structuring Neural Architecture Search with Type Theory—0
Neural Attention Search—0
Neural Inheritance Relation Guided One-Shot Layer Assignment Search—0
Neural Network Surgery: Combining Training with Topology Optimization—0
Neural Operator Search—0
NeuRN: Neuro-inspired Domain Generalization for Image Classification—0
Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading—0
NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization—0
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search—0
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search—0
OFA^2: A Multi-Objective Perspective for the Once-for-All Neural Architecture Search—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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