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 801–850 of 1915 papers

TitleStatusHype
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks—0
Robust Neural Architecture Search—0
Data Aware Neural Architecture SearchCode0
Self-Supervised learning for Neural Architecture Search (NAS)—0
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy—0
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation ImportanceCode0
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation—0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network—0
Transfer-Once-For-All: AI Model Optimization for Edge—0
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
OFA^2: A Multi-Objective Perspective for the Once-for-All Neural Architecture Search—0
Efficient Neural Architecture Search for Emotion Recognition—0
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms—0
ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement—0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
Continual Learning via Learning a Continual Memory in Vision Transformer—0
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices—0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network—0
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition—0
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs—0
FTSO: Effective NAS via First Topology Second Operator—0
A Little Bit Attention Is All You Need for Person Re-Identification—0
EvoPrompting: Language Models for Code-Level Neural Architecture Search—0
Full Stack Optimization of Transformer Inference: a Survey—0
DCLP: Neural Architecture Predictor with Curriculum Contrastive LearningCode0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—0
Search to Capture Long-range Dependency with Stacking GNNs for Graph ClassificationCode0
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
Towards Optimal Compression: Joint Pruning and Quantization—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia—0
Operation-level Progressive Differentiable Architecture SearchCode0
Improving Differentiable Architecture Search via Self-Distillation—0
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge TransferCode0
Neural Architecture Search via Two Constant Shared Weights InitialisationsCode0
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"Code0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese NetworksCode0
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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