Cost-effective Artificial Neural Networks

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Abstract

Deep neural networks (DNNs) have gained huge attention over the last several years due to their promising results in various tasks. However, due to their large model size and over-parameterization, they are recognized as being computationally demanding. Therefore, deep learning models are not well-suited to applications with limited computational resources and battery life. Current solutions to reduce computation costs mainly focus on inference efficiency while being resource-intensive during training. This Ph.D. research aims to address these challenges by developing cost-effective neural networks that can achieve decent performance on various complex tasks using minimum computational resources during training and inference of the network.

Original languageEnglish
Title of host publicationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23
EditorsEdith Elkind
Pages7071-7072
Number of pages2
ISBN (Electronic)9781956792034
DOIs
Publication statusPublished - Aug 2023
Event32nd International Joint Conferences on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: 19 Aug 202325 Aug 2023
Conference number: 32
https://ijcai-23.org/

Conference

Conference32nd International Joint Conferences on Artificial Intelligence, IJCAI 2023
Abbreviated titleIJCAI 2023
Country/TerritoryChina
CityMacao
Period19/08/2325/08/23
Internet address

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