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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1126–1150 of 1915 papers

TitleStatusHype
RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search—0
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Real-time Federated Evolutionary Neural Architecture Search—0
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation—0
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components—0
RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively—0
Reducing Inference Latency with Concurrent Architectures for Image Recognition—0
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering—0
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search—0
Regularizing Differentiable Architecture Search with Smooth Activation—0
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Reinforcement Learning with Chromatic Networks—0
RENAS: Reinforced Evolutionary Neural Architecture Search—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
Resizable Neural Networks—0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search—0
Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs—0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
Resource-Efficient Neural Architect—0
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation—0
Rethinking Co-design of Neural Architectures and Hardware Accelerators—0
Rethinking the Number of Channels for the Convolutional Neural Network—0
Retinal Vessel Segmentation via Neuron Programming—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