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 1251–1300 of 1915 papers

TitleStatusHype
ReNAS: Relativistic Evaluation of Neural Architecture SearchCode0
RHNAS: Realizable Hardware and Neural Architecture Search—0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components—0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose PredictionCode0
Zero-Cost Operation Scoring in Differentiable Architecture SearchCode0
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing—0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
Event Classification with Multi-step Machine LearningCode0
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
Discovering Better Model Architectures for Medical Query Understanding—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
Relational Graph Neural Network Design via Progressive Neural Architecture Search—0
Evolution of Activation Functions: An Empirical Investigation—0
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation—0
FNAS: Uncertainty-Aware Fast Neural Architecture Search—0
The Nonlinearity Coefficient -- A Practical Guide to Neural Architecture Design—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction—0
Ranking Architectures by Feature Extraction Capabilities—0
BatchQuant: Quantized-for-all Architecture Search with Robust QuantizerCode0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Generative Adversarial Neural Architecture Search—0
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search—0
Compatibility-aware Heterogeneous Visual Search—0
Neighborhood-Aware Neural Architecture Search—0
Graph-based Neural Architecture Search with Operation EmbeddingsCode0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search—0
Generalization Guarantees for Neural Architecture Search with Train-Validation Split—0
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference—0
Balancing Accuracy and Latency in Multipath Neural Networks—0
Inter-choice dependent super-network weights—0
Network Space Search for Pareto-Efficient Spaces—0
Carbon Emissions and Large Neural Network Training—0
Searching to Sparsify Tensor Decomposition for N-ary Relational Data—0
Making Differentiable Architecture Search less local—0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks—0
End-to-end Keyword Spotting using Neural Architecture Search and Quantization—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture SearchCode0
Autoequivariant Network Search via Group DecompositionCode0
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search—0
A Design Space Study for LISTA and Beyond—0
Searching Efficient Model-guided Deep Network for Image Denoising—0
How Powerful are Performance Predictors in 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-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