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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 1915 papers

TitleStatusHype
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion PerspectiveCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionCode1
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity EvaluationCode1
NAS-VAD: Neural Architecture Search for Voice Activity DetectionCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
NAT: Neural Architecture Transformer for Accurate and Compact ArchitecturesCode1
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture SearchCode1
Neural Architecture Generator OptimizationCode1
ChamNet: Towards Efficient Network Design through Platform-Aware Model AdaptationCode1
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance AssessmentCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
Neural Architecture Search for Lightweight Non-Local NetworksCode1
Neural Architecture Search for Spiking Neural NetworksCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
Neural Architecture Search via Bregman IterationsCode1
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-IdentificationCode1
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture SearchCode1
Neural Architecture TransferCode1
Neural Fine-Tuning Search for Few-Shot LearningCode1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture SearchCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
ColdNAS: Search to Modulate for User Cold-Start RecommendationCode1
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture SearchCode1
Offline Model-Based Optimization: Comprehensive ReviewCode1
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic ArraysCode1
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules ClassificationCode1
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture SearchCode1
One Proxy Device Is Enough for Hardware-Aware Neural Architecture SearchCode1
OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing OperatorsCode1
Differentiable Model Scaling using Differentiable TopkCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
PACE: A Parallelizable Computation Encoder for Directed Acyclic GraphsCode1
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain—0
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations—0
Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration—0
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search—0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
Anomaly-resistant Graph Neural Networks via Neural Architecture Search—0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems—0
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search—0
An Introduction to Neural Architecture Search for Convolutional Networks—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks—0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—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