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 401–450 of 1915 papers

TitleStatusHype
Optimizing edge AI models on HPC systems with the edge in the loopCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks—0
Half Search Space is All You Need—0
DimGrow: Memory-Efficient Field-level Embedding Dimension Search—0
From Hand-Crafted Metrics to Evolved Training-Free Performance Predictors for Neural Architecture Search via Genetic Programming—0
SEAL: Searching Expandable Architectures for Incremental Learning—0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
GreenFactory: Ensembling Zero-Cost Proxies to Estimate Performance of Neural Networks—0
Differentiable Channel Selection in Self-Attention For Person Re-IdentificationCode0
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers—0
NeuRN: Neuro-inspired Domain Generalization for Image Classification—0
A systematic review of challenges and proposed solutions in modeling multimodal data—0
Underwater object detection in sonar imagery with detection transformer and Zero-shot neural architecture search—0
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey—0
A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture—0
A Transformer-based Neural Architecture Search MethodCode0
Llama-Nemotron: Efficient Reasoning Models—0
Meta knowledge assisted Evolutionary Neural Architecture Search—0
A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters—0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
Regularizing Differentiable Architecture Search with Smooth Activation—0
W-PCA Based Gradient-Free Proxy for Efficient Search of Lightweight Language ModelsCode0
Transferrable Surrogates in Expressive Neural Architecture Search Spaces—0
Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?—0
Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset—0
MicroNAS: An Automated Framework for Developing a Fall Detection System—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images—0
LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection—0
Multi-Task Neural Architecture Search Using Architecture Embedding and Transfer Rank—0
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting—0
Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning—0
Neural Architecture Search by Learning a Hierarchical Search Space—0
FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search—0
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection—0
ZeroLM: Data-Free Transformer Architecture Search for Language Models—0
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM—0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
Ecological Neural Architecture Search—0
Evaluating a Novel Neuroevolution and Neural Architecture Search System—0
Architecture-Aware Minimization (A^2M): How to Find Flat Minima in Neural Architecture SearchCode0
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search—0
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
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation—0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models—0
Denoising Designs-inherited Search Framework for Image Denoising—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