Conference Information

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

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Submission Date:
2020-04-22 Extended
Notification Date:
2020-05-28
Conference Date:
2020-08-25
Location:
Dublin, Ireland
ICORE: C   Viewed: 15460   Tracked: 2   Attend: 0

Conference Partner Index (CP-I)

45.9 / 100
Ranked #2,595 of 5,684 conferences · Top 46%

#281 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
58
Submission selectivity (20%) No data - scored at the neutral baseline of 50
Editions held (20%) No data - scored at the neutral baseline of 50
Community attention (10%)
19
Public record completeness (15%)
25

Inputs used: Listed as ICORE C · Researchers following it here: 2 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 60% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-23

Call For Papers

CD-MAKE 2020 (International Cross Domain Conference for Machine Learning & Knowledge Extraction) is a ICORE C conference held in Dublin, Ireland on 2020-08-25. The paper submission deadline is 2020-04-22 (extended). Acceptance notifications are sent on 2020-05-28.

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: ❶ DATA – data fusion, preprocessing, mapping, knowledge representation, discovery ❷ LEARNING – algorithms, contextual adaptation, explainable-AI, causal reasoning ❸ VISUALIZATION – visual analytics, intelligent interfaces, human-computer interaction ❹ PRIVACY – data protection, safety, security, ethics, acceptance and social issues of ML ❺ NETWORK – graphical models, graph-based machine learning ❻ TOPOLOGY – geometrical machine learning, topological and manifold learning ❼ ENTROPY – time and machine learning, entropy-based ML
Last updated by Dou Sun on

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