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CD-MAKE 2020 : 4th International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction

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Link: https://cd-make.net
 
When Aug 25, 2020 - Aug 28, 2020
Where University College Dublin
Submission Deadline Mar 29, 2020
Notification Due May 14, 2020
Final Version Due Jun 19, 2020
Categories    machine learning   knowledge extraction   artificial intelligence
 

Call For Papers

“Augmenting Human Intelligence with Artificial Intelligence”

Call for Papers - CD-MAKE 2020
4th International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction
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 International Conference on Availability, Reliability & Security, ARES 2020

Machine learning is the workhorse of Artificial Intelligence with enormous challenges in various application domains. It needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from diverse fields.
Conference Location: University College Dublin, Dublin, Ireland
Conference Website https://cd-make.net
EasyChair Submission Link: https://easychair.org/conferences/?conf=cdmake2020
Paper Submission Deadline: May 6,2020
Author Notification: May 14, 2020
Author Registration (latest): June, 14, 2020
Camera Ready (hard deadline!): June 19, 2020
Conference: August 25 – 28, 2020


The goal of the CD-MAKE conference is to act as an innovative catalysator and to bring together researchers from the following seven thematic sub-areas in a cross-disciplinary manner, to stimulate fresh ideas and to encourage multi-disciplinary problem solving:
- DATA - Data science (data fusion, preprocessing, mapping, knowledge representation, discovery)
- LEARNING - Machine learning algorithms, contextual adaptation, explainable-AI, causal reasoning
- VISUALIZATION - and visual analytics, intelligent user interfaces, human-computer interaction
- PRIVACY - data protection, safety, security, ethics, acceptance and social issues of ML
- NETWORK - graphical models, graph-based ML
- TOPOLOGY - geometrical machine learning, topological data analysis, manifold learning
- ENTROPY - time and machine learning, entropy-based ML

Each paper will be reviewed by at least three experts. Accepted Papers will appear in a Volume of Springer Lecture Notes in Computer Science (LNCS) and there is also the opportunity to publish in our MAKE Journal: https://www.mdpi.com/journal/make

In line with CD-MAKE we organize the 2nd workshop on explainable AI (ex-AI):
https://human-centered.ai/explainable-ai-2020/

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