总产品网络结构学习的调查
Riting Xia1, Yan Zhang2, Xueyan Liu3
1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China; College of Artificial Intelligence, Jilin University, Changchun, Jilin, 130012, China.
概括
本文调查了总产品网络 (SPNs),一种深度概率模型. 它回顾了SPN结构学习算法,讨论了它们的动机,理论,分类和评估.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 总和产品网络 (SPN) 是先进的深度概率模型,在可处理性和表达性效率之间提供平衡.
- 与标准深度神经模型相比,SPN 具有更高的解释性.
- SPN 的性能和复杂性与其结构设计密切相关.
结论:
- 这项工作提供了第一个专门针对SPN结构学习的调查.
- 它旨在为人工智能和机器学习研究人员提供有价值的参考资料.
- 还讨论了SPN结构学习的未来研究方向和未解决的问题.
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