Difference between revisions of "ICTAI 2020"

From OPENRESEARCH fixed Wiki
Jump to navigation Jump to search
(modified through wikirestore by Th)
 
(modified through wikirestore by orapi)
 
(3 intermediate revisions by the same user not shown)
Line 11: Line 11:
 
|Registration link=http://ictai2020.org/registration.html
 
|Registration link=http://ictai2020.org/registration.html
 
|Acronym=ICTAI 2020
 
|Acronym=ICTAI 2020
|End date=2020/11/11
+
|End date=2020-11-11
 
|Series=ICTAI
 
|Series=ICTAI
 
|Type =Conference
 
|Type =Conference
Line 17: Line 17:
 
|State=US/MD
 
|State=US/MD
 
|City =US/MD/Baltimore
 
|City =US/MD/Baltimore
 +
|Year =2020
 
|Homepage=http://ictai2020.org/index.html
 
|Homepage=http://ictai2020.org/index.html
|Start date=2020/11/09
+
|Start date=2020-11-09
 
|Title=32nd International Conference on Tools with Artificial Intelligence
 
|Title=32nd International Conference on Tools with Artificial Intelligence
 
}}
 
}}

Latest revision as of 04:39, 6 December 2021


Event Rating

median worst
Pain1.svg Pain4.svg

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: US/MD/Baltimore, US/MD, US
Loading map...

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