24小时热门版块排行榜

>论坛更新日志 (3813)
>虫友互识 (505)
>文献求助 (334)
>导师招生 (199)
>休闲灌水 (127)
>考博 (120)
>硕博家园 (101)
>论文投稿 (83)
>论文道贺祈福 (75)
>基金申请 (64)
>博后之家 (63)
>考研 (63)
>公派出国 (62)
>教师之家 (47)
>找工作 (37)
>攻关文献(高奖励) (29)

返回列表

giannilin

铁虫 (初入文坛)

应助: 0 (幼儿园)
金币: 110.7
帖子: 31
在线: 13小时
虫号: 15598219

[交流] 【2021-10-15】【Scopus WoS】第三十七届 ACM Symposium on Applied Computing - GMLR

会议城市:
捷克共和国，布尔诺
收录:
Scopus, ACM 收录
截稿日期:
2021年10月15日

2022年第三十七届 ACM Symposium on Applied Computing (SAC 2022) Graph Models for Learning and Recognition (GMLR) Track 将于2022年4月25日至4月29日在捷克共和国布尔诺市召开。
https://phuselab.di.unimi.it/GMLR2022

会议主题
The ACM Symposium on Applied Computing (SAC 2022) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2022 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in Brno, Czech Republic. The technical track on Graph Models for Learning and Recognition (GMLR) is the first edition and is organized within SAC 2022. Graphs have gained a lot of attention in the pattern recognition community thanks to their ability to encode both topological and semantic information. Encouraged by the success of CNNs, a wide variety of methods have redefined the notion of convolution for 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

Authors of selected top papers of this track will be asked to publish an extended version in a Special Issue of a Journal (the journal will be announced soon).

程序委员会主席
Donatello Conte (University of Tours)
Giuliano Grossi (University of Milan)
Raffaella Lanzarotti (University of Milan)
Jianyi Lin (Università Cattolica del Sacro Cuore)
Jean-Yves Ramel (University of Tours)

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：2021年10月15日
录用通知期：2021年12月10日
录用论文Camera-ready （终稿版）提交日期: 2021年12月21日
SAC大会日期: 2022年4月25日至4月29日

论文投稿网站: https://www.sigapp.org/sac/sac2022/submission.html
征文启事PDF英文版: https://tiny.cc/GMLR2022

回复此楼