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 1–50 of 1915 papers

TitleStatusHype
DASViT: Differentiable Architecture Search for Vision Transformer—0
AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory ComputingCode2
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification—0
One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
Directed Acyclic Graph Convolutional Networks—0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices—0
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers—0
SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent SpaceCode0
Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models—0
Structured Pruning and Quantization for Learned Image CompressionCode0
Gibbs randomness-compression proposition: An efficient deep learningCode0
Global optimization of graph acquisition functions for neural architecture search—0
CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems—0
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
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
SEAL: Searching Expandable Architectures for Incremental Learning—0
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
A systematic review of challenges and proposed solutions in modeling multimodal data—0
NeuRN: Neuro-inspired Domain Generalization for Image Classification—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 Transformer-based Neural Architecture Search MethodCode0
A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture—0
Llama-Nemotron: Efficient Reasoning Models—0
A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters—0
Meta knowledge assisted Evolutionary Neural Architecture Search—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
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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