2023 International Conference on Frontiers of Mathematics and Artificial Intelligence(CFMAI 2023)

Website https://www.cfmai.org/ | Edit Freely

Category Mathematics; Artificial Intelligence

Deadline: August 15, 2023 | Date: September 22, 2023-September 24, 2023

Venue/Country: Hangzhou, China, China

Updated: 2023-06-05 16:57:00 (GMT+9)

Call For Papers - CFP

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.

Excellent papers will be recommended to be published in the journal.

★Keynote Speakers

Prof. Weifeng Gao——Xidian University, China

★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

PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2023

★CONTACT US

Ms. Riva H. W. Wong

Email: infocfais.org

Website: www.cfais.org

★Topics of Interest

Approximate inference

Bayesian learning

Business process intelligence

Causal models

Classification

Clustering

Density estimation

Federated learning

Gaussian processes

Generalization and regularization

Graphical models

High-performance computation

Intelligent optimization

Kernel and spectral methods

Large-scale optimization algorithm

Logic and probability

Manifolds learning

Multi-agent systems

Non-Bayesian models and estimation

Nonparametric models

No-regret learning

Optimization for deep learning

Privacy-preserving learning

Reinforcement learning

Software and applications of artificial intelligence

Sparsity and compressed sensing

Statistical and computational learning theory

Statistical optimization

Structural learning and prediction

Unsupervised and semi-supervised learning