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 1751–1800 of 1915 papers

TitleStatusHype
AGAN: Towards Automated Design of Generative Adversarial Networks—0
Densely Connected Search Space for More Flexible Neural Architecture SearchCode0
Posterior-Guided Neural Architecture SearchCode0
Clustering and Classification Networks—0
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural ArchitecturesCode0
Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents—0
A Study of the Learning Progress in Neural Architecture Search Techniques—0
Hardware Aware Neural Network Architectures using FbNetCode0
Sample-Efficient Neural Architecture Search by Learning Action Space—0
Scalable Neural Architecture Search for 3D Medical Image Segmentation—0
Continual and Multi-Task Architecture SearchCode0
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
One-Shot Neural Architecture Search via Compressive SensingCode0
AutoGrow: Automatic Layer Growing in Deep Convolutional NetworksCode0
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation—0
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks—0
Butterfly Transform: An Efficient FFT Based Neural Architecture DesignCode0
MFAS: Multimodal Fusion Architecture SearchCode0
RENAS: Reinforced Evolutionary Neural Architecture Search—0
Efficient Neural Architecture Search via Proximal IterationsCode0
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers—0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
Dynamic Cell Structure via Recursive-Recurrent Neural Networks—0
Network Pruning via Transformable Architecture SearchCode0
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture SearchCode0
DARC: Differentiable ARchitecture Compression—0
Multinomial Distribution Learning for Effective Neural Architecture SearchCode0
DeepSwarm: Optimising Convolutional Neural Networks using Swarm IntelligenceCode0
AutoDispNet: Improving Disparity Estimation With AutoMLCode0
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search—0
Deep Neural Architecture Search with Deep Graph Bayesian OptimizationCode0
BayesNAS: A Bayesian Approach for Neural Architecture Search—0
Dynamic Routing Networks—0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
EENA: Efficient Evolution of Neural ArchitectureCode0
Single-Path NAS: Device-Aware Efficient ConvNet Design—0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS—0
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation—0
Fast and Reliable Architecture Selection for Convolutional Neural NetworksCode0
Differentiable Architecture Search with Ensemble Gumbel-Softmax—0
A Survey on Neural Architecture Search—0
Single Shot Neural Architecture Search Via Direct Sparse Optimization—0
Probabilistic Model-Based Dynamic Architecture Search—0
AdaNet: A Scalable and Flexible Framework for Automatically Learning EnsemblesCode0
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and EvaluationCode0
TreeGrad: Transferring Tree Ensembles to Neural NetworksCode0
D-VAE: A Variational Autoencoder for Directed Acyclic GraphsCode0
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural NetworksCode0
Show:102550
← PrevPage 36 of 39Next →
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