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CD-MAKE 2021 : Cross Domain Conference for Machine Learning and Knowledge Extraction

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Link: https://cd-make.net/about/
 
When Aug 17, 2021 - Aug 20, 2021
Where Virtual Conference
Submission Deadline Apr 15, 2021
Notification Due May 27, 2021
Final Version Due Jun 18, 2021
 

Call For Papers

5th International Cross Domain Conference for
Machine Learning & Knowledge Extraction (CD-MAKE 2021)
CD-MAKE 2021 Call for Papers (txt, pdf)

CD-MAKE is a joint effort of IFIP TC 5, TC 12, IFIP WG 8.4, WG 8.9 and WG 12.9 and is held in conjunction with the 16th International Conference on Availability, Reliability and Security ARES 2021
August 17th – August 20th, 2021.

The 2021 ARES & CD-MAKE Organization is closely monitoring the ongoing COVID-19 situation. The safety and well-being of all conference participants is our top priority. After studying and evaluating the announcements, guidance, and news released by relevant national departments, we are prepared to convert ARES & CD-MAKE 2021 into an all-digital conference experience if needed. The dates of the conference would remain the same.

Machine learning and Knowledge Extraction (MAKE) is the workhorse of Artificial Intelligence (AI). Successful human-centered AI needs a concerted effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts cross-domain.

The goal of the CD-MAKE conference is to act as a catalysator, to bring together academia and industry in a cross-disciplinary manner, to stimulate fresh ideas and to support human-centered AI:

1) DATA – data fusion, preprocessing, mapping, knowledge representation, environments, etc.
2) LEARNING – algorithms, contextual adaptation, causal reasoning, transfer learning, etc.
3) VISUALIZATION – intelligent interfaces, human-AI interaction, dialogue systems, explanation interfaces, etc.
4) PRIVACY – data protection, safety, security, reliability, verifiability, trust, ethics and social issues, etc.
5) NETWORK – graphical models, graph-based machine learning, Bayesian inference, etc.
6) TOPOLOGY – geometrical machine learning, topological and manifold learning, etc.
7) ENTROPY – time and machine learning, entropy-based learning, etc.

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