Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains
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混合和积网络：混合域的深层架构
DOI：
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发表时间：
2018
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通讯作者：
K. Kersting
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作者：
Alejandro Molina;Antonio Vergari;Nicola Di Mauro;Sriraam Natarajan;F. Esposito;K. Kersting
虽然收集和存储了各种混合数据--从个人数据，跨面板和科学数据，到公共和商业数据--，但为这些混合领域构建概率图模型变得更加困难。用户花费大量的时间来识别随机变量的参数形式（高斯，泊松，Logit等）。参与并学习混合模型。为了使这项艰巨的任务变得更容易，我们提出了第一个可训练的概率深度架构，用于混合域，其特点是易于处理的查询。它是基于和产品网络（SPN）与分段多项式叶分布连同新颖的非参数分解和调理步骤，使用Hirscher-Gebelein-Renyi最大相关系数。这减轻了用户决定先验的随机变量的参数形式，但仍然是足够的表达，以有效地近似任何分布，并允许有效的学习和inference.Our实验表明，该架构，称为混合SPN，确实可以捕捉复杂的分布在广泛的混合域。
While all kinds of mixed data---from personal data, over panel and scientific data, to public and commercial data---are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models. To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise polynomial leaf distributions together with novel nonparametric decomposition and conditioning steps using the Hirschfeld-Gebelein-Renyi Maximum Correlation Coefficient. This relieves the user from deciding a-priori the parametric form of the random variables but is still expressive enough to effectively approximate any distribution and permits efficient learning and inference.Our experiments show that the architecture, called Mixed SPNs, can indeed capture complex distributions across a wide range of hybrid domains.