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

  in Conferences   Posted on February 1, 2020

Conference Information

Submission Deadline Sunday 29 Mar 2020 Proceedings indexed by :
Conference Dates Aug 25, 2020 - Aug 28, 2020
Conference Address University College Dublin, Ireland
Conference & Submission Link
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference 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

EasyChair Submission Link:

Paper Submission Deadline: March 29,2020

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:

In line with CD-MAKE we organize the 2nd workshop on explainable AI (ex-AI):

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