SpeakerJuncai He 何俊材TimeTues. 9:50-11:25 amWed. 13:30-15:05April 15-June 4, 2025VenueC654Shuangqing Complex Building ACourse description This course will systematically explore and analyze some key theories and algorithms in deep learning from a numerical analysis perspective. Traditionally, the foundational theory in deep learning is largely concerned with approximation and generalization e...
Description: This course introduces how to develop deep generative models (DGMs) by integrating probabilistic graphical models and deep learning to generate realistic data including images, texts, graphs, etc. Course contents include 1) basics of probabilistic graphical models, including Bayesian network and Markov random field; 2) posterior inference methods, including message passing, variat...
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