Difference between revisions of "ICTAI 2020"

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{{Event
 
{{Event
 +
|Acronym=ICTAI 2020
 +
|Title=32nd International Conference on Tools with Artificial Intelligence
 +
|Series=ICTAI
 +
|Type=Conference
 
|Field=Artificial intelligence
 
|Field=Artificial intelligence
 +
|Start date=2020/11/09
 +
|End date=2020/11/11
 
|Submission deadline=2020/06/10
 
|Submission deadline=2020/06/10
 +
|Homepage=http://ictai2020.org/index.html
 +
|City=Baltimore
 +
|State=Maryland
 +
|Country=USA
 
|Paper deadline=2020/06/10
 
|Paper deadline=2020/06/10
 
|Notification=2020/08/16
 
|Notification=2020/08/16
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|has program chair=Shimei Pan
 
|has program chair=Shimei Pan
 
|Registration link=http://ictai2020.org/registration.html
 
|Registration link=http://ictai2020.org/registration.html
|Acronym=ICTAI 2020
 
|End date=2020/11/11
 
|Series=ICTAI
 
|Type =Conference
 
|Country=US
 
|State=US/MD
 
|City =US/MD/Baltimore
 
|Homepage=http://ictai2020.org/index.html
 
|Start date=2020/11/09
 
|Title=32nd International Conference on Tools with Artificial Intelligence
 
 
}}
 
}}
 
==Topics==
 
==Topics==

Revision as of 18:14, 3 November 2021


Event Rating

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List of all ratings can be found at ICTAI 2020/rating

ICTAI 2020
32nd International Conference on Tools with Artificial Intelligence
Event in series ICTAI
Dates 2020/11/09 (iCal) - 2020/11/11
Homepage: http://ictai2020.org/index.html
Submitting link: http://ictai2020.org/submission.html
Location
Location: Baltimore, Maryland, USA
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Important dates
Papers: 2020/06/10
Submissions: 2020/06/10
Notification: 2020/08/16
Camera ready due: 2020/09/20
Registration link: http://ictai2020.org/registration.html
Committees
General chairs: Miltos Alamaniotis
PC chairs: Shimei Pan
Table of Contents

Topics

AI Foundations

  • Machine Learning and Data Mining
  • Evolutionary computing, Bayesian and Neural Networks
  • Pre-processing, Dimension Reduction and Feature Selection
  • Decision/Utility Theory and Decision Optimization
  • Learning Graphical Models and Complex Networks
  • Search, SAT, and CSP Active, Cost-Sensitive, Semi-Supervised, Multi-Instance, Multi-Label and Multi-Task Learning
  • Description Logic and Ontologies
  • Transfer/Adaptive, Rational and Structured Learning

AI in Domain-specific Applications

  • Preference/Ranking, Ensemble, and Reinforcement Learning

AI in Computational Biology, Medicine and Biomedical Applications

  • Knowledge Representation, Reasoning and Cognitive Modelling