Neural Graphical Models
This work addresses computational bottlenecks in graphical modeling for researchers and practitioners, though it appears incremental as it builds on existing methods with neural network enhancements.
The paper tackles the computational limitations of probabilistic graphical models by introducing Neural Graphical Models (NGMs), which use neural networks to represent complex feature dependencies with efficient algorithms, and demonstrates their capability on tasks like Gaussian graphical models and real-world data analysis.
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.