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小木虫论坛-学术科研互动平台 » 学术交流区 » 学术会议 » 会议检索 » 【2023-09-15】【Scopus WoS】第三十九届 ACM Symposium on Applied Computing - GMLR
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[交流] 【2023-09-15】【Scopus WoS】第三十九届 ACM Symposium on Applied Computing - GMLR

会议城市
阿维拉，西班牙
收录
scopus，acm，wos 收录
截稿日期
2023年9月15日
https://phuselab.di.unimi.it/gmlr2024

2024年第三十九届 acm symposium on applied computing (sac 2024) graph models for learning and recognition (gmrl) track 将于2024年4月8日至12日在西班牙阿维拉市召开。

会议主题
the acm symposium on applied computing (sac 2024) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. sac 2024 is sponsored by the acm special interest group on applied computing (sigapp), and will be held in avila, spain. the technical track on graph models for learning and recognition (gmlr) is the third edition and is organized within sac 2024. graphs have gained a lot of attention in the pattern recognition community thanks to their ability to encode both topological and semantic information. despite their invaluable descriptive power, their arbitrarily complex structured nature poses serious challenges when they are involved in learning systems. some (but not all) of challenging concerns are: a non-unique representation of data, heterogeneous attributes (symbolic, numeric, etc.), and so on.
in recent years, due to their widespread applications, graph-based learning algorithms have gained much research interest. encouraged by the success of cnns, a wide variety of methods have redefined the notion of convolution and related operations on graphs. these new approaches have in general enabled effective training and achieved in many cases better performances than competitors, though at the detriment of computational costs. typical examples of applications dealing  with graph-based representation are: scene graph generation, point clouds classification, and action recognition in computer vision; text classification, inter-relations of documents or words to infer document labels in natural language processing; forecasting traffic speed, volume or the density of roads in traffic networks, whereas in chemistry researchers apply graph-based algorithms to study the graph structure of molecules/compounds.

this track intends to focus on all aspects of graph-based representations and models for learning and recognition tasks. gmlr spans, but is not limited to, the following topics:
● graph neural networks: theory and applications
● deep learning on graphs
● graph or knowledge representational learning
● graphs in pattern recognition
● graph databases and linked data in ai
● benchmarks for gnn
● dynamic, spatial and temporal graphs
● graph methods in computer vision
● human behavior and scene understanding
● social networks analysis
● data fusion methods in gnn
● efficient and parallel computation for graph learning algorithms
● reasoning over knowledge-graphs
● interactivity, explainability and trust in graph-based learning
● probabilistic graphical models
● biomedical data analytics on graphs
the track committee is working to organize a journal special issue, to which the authors of selected top papers of this track will be invited for an extended version.

程序委员会主席
alessandro d'amelio (university of milan)
giuliano grossi (university of milan)
raffaella lanzarotti (university of milan)
jianyi lin (università cattolica del sacro cuore)

程序委员
laura-bianca bilius (university of suceava)
sathya bursic (university of milano-bicocca)
antonella carbonaro (university of bologna)
vittorio cuculo (university of modena and reggio emilia)
samuel feng (sorbonne university abu dhabi)
gabriele gianini (university of milan)
francesco isgrò (university of naples federico ii)
sotirios kentros (salem state university)
giosuè lo bosco (university of palermo)
maurice pagnucco (university of new south wales)
sabrina patania (university of milan)
alessandro provetti (birkbeck university of london)
jean-yves ramel (university of tours)
ryan a. rossi (adobe research)
alessandro sperduti (university of padua)
(others to be confirmed)

征文要求
邀请作者提交未发表的原创研究论文和应用论文。论文正文不能包含作者姓名或地址，以便于双盲审查。投稿撰写论文必需是英语。
需要了解提交程序的更多信息，请查询会议网站。
sac报告缺席政策：录用并完成注册的全文和张贴将收录到会议论文集。如果本人无法参加，需请其他同事代做报告，否则全文不能被收入acm数字图书馆。

主要日期
论文全文截稿期已延长至 ：2023年9月15日
录用通知期 ：2023年10月30日
录用论文camera-ready （终稿版）提交日期: 2023年11月30日
sac大会日期: 2024年4月8日至12日

论文投稿网站: https://www.sigapp.org/sac/sac2024/submission.php
征文启事pdf英文版: https://tiny.cc/gmlr2024-cfp

截稿日期延长
论文全文截稿期已延长至 ：2023年9月29日
征文启事pdf英文版: https://tiny.cc/GMLR2024-CfP-2

[ Last edited by giannilin on 2023-9-18 at 05:44 ]
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