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 1–10 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
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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
1NAT-M4Accuracy (%)89.4—Unverified
2NAT-M3Accuracy (%)89—Unverified
3NAT-M2Accuracy (%)88.5—Unverified
4NAT-M1Accuracy (%)87.4—Unverified
5Balanced MixtureAccuracy (% )84.73—Unverified