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

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Showing 51–100 of 1915 papers

TitleStatusHype
FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search—0
Neural Architecture Search by Learning a Hierarchical Search Space—0
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter DetectionCode0
ZeroLM: Data-Free Transformer Architecture Search for Language Models—0
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM—0
Offline Model-Based Optimization: Comprehensive ReviewCode1
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
Evaluating a Novel Neuroevolution and Neural Architecture Search System—0
Ecological Neural Architecture Search—0
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search—0
Architecture-Aware Minimization (A^2M): How to Find Flat Minima in Neural Architecture SearchCode0
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation—0
ZO-DARTS++: An Efficient and Size-Variable Zeroth-Order Neural Architecture Search AlgorithmCode0
NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization—0
UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture SearchCode2
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models—0
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation—0
Learning Novel Transformer Architecture for Time-series Forecasting—0
Denoising Designs-inherited Search Framework for Image Denoising—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
Neural Attention Search—0
Cognitive Neural Architecture Search Reveals Hierarchical Entailment—0
InterGridNet: An Electric Network Frequency Approach for Audio Source Location Classification Using Convolutional Neural Networks—0
AttentionSmithy: A Modular Framework for Rapid Transformer Development and Customization—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks—0
Efficient Global Neural Architecture SearchCode0
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network WeightsCode0
The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices—0
Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection—0
Low-Rank Adapters Meet Neural Architecture Search for LLM CompressionCode0
Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm—0
SuperSAM: Crafting a SAM Supernetwork via Structured Pruning and Unstructured Parameter PrioritizationCode0
Automated Heterogeneous Network learning with Non-Recursive Message PassingCode0
Neural Architecture Codesign for Fast Physics ApplicationsCode0
Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and InsightsCode1
TART: Token-based Architecture Transformer for Neural Network Performance PredictionCode0
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights—0
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection—0
NADER: Neural Architecture Design via Multi-Agent Collaboration—0
Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural PerspectiveCode0
Graph Neural Networks Are Evolutionary Algorithms—0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation—0
Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions—0
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural NetworksCode0
HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning—0
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time SeriesCode0
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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