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

Website https://cd-make.net | Edit Freely

Category machine learning; knowledge extraction; artificial intelligence

Deadline: March 29, 2020 | Date: August 25, 2020-August 28, 2020

Venue/Country: University College Dublin, Ireland

Updated: 2020-01-29 17:09:58 (GMT+9)

Call For Papers - CFP

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: 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: 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/