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描述
概率图模型与神经生成模型 介绍生成模型的理论基础及其在预测、知识发现与创意设计中的应用。概率图模型（PGMs）的基础、学习与推理，包括隐马尔可夫模型、混合模型与潜在Dirichlet分配等传统PGM。无向神经生成模型（NGMs），包括马尔可夫随机场及玻尔兹曼机的变体。有向NGMs，包括赫尔姆霍兹机与深度置信网、变分自编码器与生成对抗网络。
原文参考文本
Probabilistic Graphical Models and Neural Generative Models Introduction to the theoretical foundations of generative models and their applications in prediction, knowledge discovery, and creative design. Foundations of probabilistic graphical models (PGMs), learning, and inference in traditional PGMs such as hidden Markov models, mixture models, and latent Dirichlet allocation. Undirected neural generative models (NGMs), including Markov random fields, and variants of Boltzmann machines. Directed NGMs, including Helmholtz machines and deep belief nets, variational autoencoders, and generative adversarial networks.
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来源参考 : https://brocku.ca/webcal/2024/graduate/cosc.html