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【2022-10-24】【Scopus WoS】第三十八届 ACM Symposium on Applied Computing - GMLR
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截稿日期延长 会议城市 爱沙尼亚，塔林 收录 scopus，acm，wos 收录 截稿日期 已延长至2022年10月24日 https://phuselab.di.unimi.it/gmlr2023 2023年第三十八届 acm symposium on applied computing (sac 2023) graph models for learning and recognition (gmlr) track 将于2023年3月27日至4月2日在爱沙尼亚共和国塔林市召开。 会议主题 the acm symposium on applied computing (sac 2023) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. sac 2023 is sponsored by the acm special interest group on applied computing (sigapp), and will be held in tallinn, estonia. the technical track on graph models for learning and recognition (gmlr) is the second edition and is organized within sac 2023. 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 donatello conte (university of tours) 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 ● annalisa barla (university of genoa) ● davide boscaini (bruno kessler foundation) ● antonella carbonaro (university of bologna) ● vittorio cuculo (university of milan) ● samuel feng (sorbonne university abu dhabi) ● gabriele gianini (university of milan) ● andreas henschel (khalifa university) ● francesco isgrò (university of naples) ● giosuè lo bosco (university of palermo) ● alessio micheli (university of pisa) ● carlos oliver (eth zürich) ● maurice pagnucco (university of new south wales) ● jean-yves ramel (university of tours) ● ryan a. rossi (adobe research) (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 ：2022年10月24日 录用通知期 ：2022年12月5日 录用论文camera-ready （终稿版）提交日期: 2022年12月13日 sac大会日期: 2023年3月27日至4月2日 论文投稿网站: https://www.sigapp.org/sac/sac2023/submission.html 征文启事pdf英文版: https://tiny.cc/gmlr2023-cfp [ last edited by giannilin on 2022-10-13 at 20:05 ] 截稿日期延长至2022年10月24日 [ last edited by giannilin on 2022-10-24 at 06:18 ] <color=red>截稿日期延长至2022年10月31日</color> [ Last edited by giannilin on 2022-10-25 at 00:43 ] |
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