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EI-CFAIS 2023 : 2023 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2023) | |||||||||||||||
Link: http://www.cfais.org/ | |||||||||||||||
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Call For Papers | |||||||||||||||
★2023 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2023)--Ei Compendex & Scopus—Call for paper
August 18-20, 2023|Nanjing, China|Website: www.cfais.org ★CFAIS 2023 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 2023 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. Huiyu Zhou, University of Leicester, UK ★Program Preview/ Program at a glance August 18: Registration + Icebreaker Reception August 19: Opening Ceremony+ KN Speech+ Technical Sessions August 20: Technical Sessions+ Half day tour/Lab tours ★Paper Submission 1.PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2023 ★CONTACT US Ms. Willa P. P. Wong Email: info@cfais.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 |
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