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

Showing 676700 of 1915 papers

TitleStatusHype
Multi-scale Attentive Image De-raining Networks via Neural Architecture SearchCode0
Multi-Prior Learning via Neural Architecture Search for Blind Face RestorationCode1
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization0
Prior-Guided One-shot Neural Architecture SearchCode1
FEATHERS: Federated Architecture and Hyperparameter Search0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection0
FedorAS: Federated Architecture Search under system heterogeneity0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
Shapley-NAS: Discovering Operation Contribution for Neural Architecture SearchCode1
NAS-Bench-Graph: Benchmarking Graph Neural Architecture SearchCode1
FreeREA: Training-Free Evolution-based Architecture SearchCode1
DFG-NAS: Deep and Flexible Graph Neural Architecture SearchCode0
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge NodesCode0
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
EmProx: Neural Network Performance Estimation For Neural Architecture SearchCode0
Exploring the Intersection between Neural Architecture Search and Continual Learning0
SDQ: Stochastic Differentiable Quantization with Mixed Precision0
Neural Prompt SearchCode2
Towards Self-supervised and Weight-preserving Neural Architecture SearchCode0
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning0
Transfer Learning based Search Space Design for Hyperparameter Tuning0
AGNAS: Attention-Guided Micro- and Macro-Architecture SearchCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified