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 451–475 of 1915 papers

TitleStatusHype
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Efficient Differentiable Neural Architecture Search with Model Parallelism—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Automatic Routability Predictor Development Using Neural Architecture Search—0
Neural Architecture Search using Property Guided Synthesis—0
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity—0
Efficient Evaluation Methods for Neural Architecture Search: A Survey—0
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
Deep Demosaicing for Edge Implementation—0
DASViT: Differentiable Architecture Search for Vision Transformer—0
Deep End2End Voxel2Voxel Prediction—0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification—0
Deep Learning Scaling is Predictable, Empirically—0
DAS: Neural Architecture Search via Distinguishing Activation Score—0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network—0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
DARTS without a Validation Set: Optimizing the Marginal Likelihood—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs—0
An Approach for Efficient Neural Architecture Search Space Definition—0
Deep reinforcement learning in medical imaging: A literature review—0
Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions—0
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution—0
Adaptive quantization with mixed-precision based on low-cost proxy—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified