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

Showing 110 of 1915 papers

TitleStatusHype
DASViT: Differentiable Architecture Search for Vision Transformer0
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 Classification0
One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering0
Directed Acyclic Graph Convolutional Networks0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices0
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EEEA-Net-C (b=5)+ COParams3.6Unverified
2ENAS + c/oPercentage error2.89Unverified
3DARTS + c/oPercentage error2.83Unverified
4NAT-M1Percentage error2.6Unverified
5GATES + c/oPercentage error2.58Unverified
6arch2vecPercentage error2.56Unverified
7Soft Parameter SharingPercentage error2.53Unverified
8CATEPercentage error2.46Unverified
9NAS-RL-A + c/oPercentage error2.4Unverified
10PathLevel EAS + c/oPercentage error2.3Unverified