![]() |
| |||||||||||||||
EI-CFAIS 2022 : 2022 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2022) | |||||||||||||||
Link: http://www.cfais.org/ | |||||||||||||||
| |||||||||||||||
Call For Papers | |||||||||||||||
★2022 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2022)--Ei Compendex & Scopus—Call for paper
December 2-4, 2022|Beijing, China|Website: www.cfais.org ★CFAIS 2022 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 Accepted and presented papers of CFAIS 2022 will be published in the digital conference proceedings which will submitted to Ei Compendex, Scopus, CPCI, Google Scholar and other major databases for index. A selection of papers will be recommended to be published in the journal. ★Keynote Speakers Prof. Guoyin Wang, Chongqing University of Posts and Telecommunications, China Prof.Yingxu Wang, University of Calgary, Canada Prof. Tianrui LI, Southwest Jiaotong University, China Prof.Jocelyn Chanussot, Chinese Academy of Sciences, China Dr.Ahmad P. Tafti, University of Southern Maine, USA ★Program Preview/ Program at a glance December 2: Registration + Icebreaker Reception December 3: Opening Ceremony+ KN Speech+ Technical Sessions December 4: Technical Sessions+ Half day tour/Lab tours ★Paper Submission 1.PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2022 2.Submit Via email directly to: cfais@hksra.org ★CONTACT US Ms. Willa P. P. Wong Email: cfais@hksra.org Website: www.cfais.org Call for papers(http://www.cfais.org/cfp.html): Algorithms and architectures for high-performance computation Manifolds and embedding Approximate inference Multi-agent systems Bayesian models and estimation No-regret learning Business Process Intelligence Non-Bayesian models and estimation Causality Nonparametric models Classification Regression Clustering Reinforcement learning, planning, control Deep learning including optimization Relational learning Density estimation Software for and applications of AI and statistics Game theory Solicited topics Gaussian processes Sparsity and compressed sensing Generalization and architectures Statistical and computational learning theory Graphical models Structured prediction Including learning with privacy and fairness Topic models Interpretability and robustness Trustworthy learning Kernel methods Unsupervised and semi-supervised learning Logic and probability |
|