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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 1001–1050 of 1915 papers

TitleStatusHype
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models—0
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative GainCode0
Generic Neural Architecture Search via RegressionCode1
Efficient Neural Architecture Search with Performance Prediction—0
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
FLASH: Fast Neural Architecture Search with Hardware Optimization—0
WAS-VTON: Warping Architecture Search for Virtual Try-on Network—0
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models—0
Homogeneous Architecture Augmentation for Neural PredictorCode0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
Experiments on Properties of Hidden Structures of Sparse Neural NetworksCode0
μDARTS: Model Uncertainty-Aware Differentiable Architecture Search—0
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies—0
A Novel Evolutionary Algorithm for Hierarchical Neural Architecture SearchCode0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy—0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
LANA: Latency Aware Network Acceleration—0
Core-set Sampling for Efficient Neural Architecture Search—0
Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration—0
Bag of Tricks for Neural Architecture Search—0
GLiT: Neural Architecture Search for Global and Local Image TransformerCode1
Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search—0
Integrating Large Circular Kernels into CNNs through Neural Architecture SearchCode1
Mutation is all you need—0
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture SearchCode0
Rapid Neural Architecture Search by Learning to Generate Graphs from DatasetsCode1
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
Poisoning the Search Space in Neural Architecture Search—0
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image SegmentationCode1
ViTAS: Vision Transformer Architecture SearchCode1
AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation—0
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise—0
Multi-objective Asynchronous Successive HalvingCode3
NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems—0
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization—0
iDARTS: Differentiable Architecture Search with Stochastic Implicit GradientsCode1
Removing Raindrops and Rain Streaks in One GoCode1
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
HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
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
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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