Graphs are ubiquitous and are often used to understand the dynamics of a
system. Probabilistic Graphical Models comprising Bayesian and Markov networks,
and Conditional Independence graphs are some of the popular graph
representation techniques. They can model relationships between features
(nodes) together with the underlying distribution. Although theoretically these
models can represent very complex dependency functions, in practice often
simplifying assumptions are made due to computational limitations associated
with graph operations. This work introduces Neural Graphical Models (NGMs)
which attempt to represent complex feature dependencies with reasonable
computational costs. Specifically, given a graph of feature relationships and
corresponding samples, we capture the dependency structure between the features
along with their complex function representations by using neural networks as a
multi-task learning framework. We provide efficient learning, inference and
sampling algorithms for NGMs. Moreover, NGMs can fit generic graph structures
including directed, undirected and mixed-edge graphs as well as support mixed
input data types. We present empirical studies that show NGMs' capability to
represent Gaussian graphical models, inference analysis of a lung cancer data
and extract insights from a real world infant mortality data provided by CDC.

本研究介绍了神经图模型，该模型采用神经网络作为多任务学习框架，通过表示节点之间的依赖结构及其复杂的函数表示来捕获特征之间的完整的复杂依赖关系，并提供 NGMs 的有效学习、推理和采样算法。NGMs 可以适应包括有向、无向和混合边缘图在内的通用图结构，可以处理混合输入数据类型。经验研究表明，NGMs 能够代表高斯图模型，并从由 CDC 提供的真实世界婴儿死亡数据中提取见解。