-------- Forwarded Message -------- Subject: [WI] [CFP] The 37th ACM Symposium on Applied Computing (SAC 2022) - Graph Models for Learning and Recognition (GMLR) Track Date: Fri, 01 Oct 2021 11:49:39 +0200 From: fulvio.frati@unimi.it Reply-To: fulvio.frati@unimi.it To: wi@lists.uni-karlsruhe.de
*** ACM SAC 2022 ***
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Graph Models for Learning and Recognition (GMLR) Track
The 37th ACM Symposium on Applied Computing (SAC 2022)
April 25-29, 2022, Brno, Czech Republic
http://phuselab.di.unimi.it/GMLR2022 http://phuselab.di.unimi.it/GMLR2022
MOTIVATIONS AND TOPICS
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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).
IMPORTANT DATES
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Submission of regular papers: October 15, 2021
Notification of acceptance/rejection: December 10, 2021
Camera-ready copies of accepted papers: December 21, 2021
SAC Conference: April 25 - 29, 2022
SUBMISSION GUIDELINES
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Authors are invited to submit original and unpublished papers of research and applications for this track. The author(s) name(s) and address(es) must not appear in the body of the paper, and self-reference should be in the third person. This is to facilitate double-blind review. Please, visit the website for more information about submission
SAC NO-SHOW POLICY
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Paper registration is required, allowing the inclusion of the paper/poster in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for the paper/poster to be included in the ACM digital library. No-show of registered papers and posters will result in excluding them from the ACM digital library.
TRACK CHAIRS
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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)
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