Hypernetworks: Neural Networks for Hierarchical Data
Summary
Hypernetworks presents a method to handle hierarchical data by learning dataset embeddings and using a hypernetwork to generate dataset-specific parameters for the main network. The article motivates dataset-adaptive modeling, provides a minimal implementation approach, discusses training on synthetic Planck-law data, and explores predictions for new datasets along with limitations and future Bayesian directions.