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EI-CFAIS 2020 : 2020 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2020)

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Link: http://www.cfais.org/
 
When Jul 17, 2020 - Jul 19, 2020
Where Seoul, South Korea
Submission Deadline May 31, 2020
Notification Due May 31, 2020
Final Version Due May 31, 2020
 

Call For Papers

★2020 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2020)--Ei Compendex & Scopus—Call for paper 
July 17-19, 2020|Seoul, South Korea|Website: www.cfais.org

★CFAIS 2020 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Artificial Intelligence and Statistics can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field. Leading researchers and industry experts from around the globe will be presenting the latest studies through papers and oral presentations. 


★Publication and Indexing
All accepted papers will be published in the digital conference proceedings which will be sent to be Indexed by  all major citation databases such as Ei Compendex, SCOPUS, Google Scholar, Cambridge Scientific Abstracts (CSA), Inspec, SCImago Journal & Country Rank (SJR), EBSCO, CrossRef,  Thomson Reuters (WoS), etc. 
A selection of papers will be recommended to be published in international journals.

★Program Preview/ Program at a glance
July 17: Registration + Icebreaker Reception
July 18: Opening Ceremony+ KN Speech+ Technical Sessions
July 19: Technical Sessions+ Half day tour/Lab tours

★Paper Submission
1.PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2020
2.Submit Via email directly to: cfais@hksra.org

★CONTACT US
Ms. Anna H. M. Wong
Email: cfais@hksra.org
Website: www.cfais.org

Call for papers(http://www.cfais.org/cfp.html):
Solicited topics include, but are not limited to:

Algorithms and architectures for high-performance computation in AI and statistics

Algorithms and architectures for high-performance computation in AI and statistics

Bayesian models and estimation (graphical models, causality, Gaussian processes, approximate inference, ...)

Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models, ...

Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models, ...

Deep learning including optimization, generalization and architectures

Game theory, no-regret learning, multi-agent systems

Game theory, no-regret learning, multi-agent systems

Models and estimation: graphical models, causality, Gaussian processes, approximate inference, kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing, ...

Non-Bayesian models and estimation (kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing, ...)

Reinforcement learning, planning, control

Reinforcement learning, planning, control

Software for and applications of AI and statistics

Software for and applications of AI and statistics

Structured prediction, relational learning, logic and probability

Structured prediction, relational learning, logic and probability

Trustworthy learning, including learning with privacy and fairness, interpretability, and robustness

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