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 17761800 of 1915 papers

TitleStatusHype
Yoga Pose Classification Using Transfer Learning0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification0
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search0
Deep End2End Voxel2Voxel Prediction0
EvoPrompting: Language Models for Code-Level Neural Architecture Search0
Exascale Deep Learning to Accelerate Cancer Research0
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays0
Explicit Learning Topology for Differentiable Neural Architecture Search0
Stretchable Cells Help DARTS Search Better0
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence0
Exploiting Operation Importance for Differentiable Neural Architecture Search0
Exploring Complicated Search Spaces with Interleaving-Free Sampling0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS0
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search0
Deep Demosaicing for Edge Implementation0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity0
Self Semi Supervised Neural Architecture Search for Semantic Segmentation0
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks0
Exploring single-path Architecture Search ranking correlations0
Exploring the Manifold of Neural Networks Using Diffusion Geometry0
Self-supervised Cross-silo Federated Neural Architecture Search0
Extensible and Efficient Proxy for Neural Architecture Search0
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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