Neural Graphical Models

TL;DR

Neural Graphical Models (NGMs) are introduced as a flexible, efficient approach to model complex feature dependencies in graphical models using neural networks, supporting various graph types and data.

Contribution

This work presents NGMs, a novel neural network-based framework that efficiently models complex dependencies in diverse graphical structures with practical inference and learning algorithms.

Findings

NGMs can accurately represent Gaussian graphical models.

NGMs perform effective inference on lung cancer data.

NGMs extract meaningful insights from infant mortality data.

Abstract

Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretically these models can represent very complex dependency functions, but in practice often simplifying assumptions are made due to computational limitations associated with graph operations. In this work we introduce Neural Graphical Models (NGMs) which attempt to represent complex feature dependencies with reasonable computational costs. 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 a neural network as a multi-task learning framework. We provide efficient learning, inference and sampling algorithms. 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, perform inference analysis of a lung cancer data and extract insights from a real world infant mortality data provided by Centers for Disease Control and Prevention.