Title: IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks,
Abstract: Deep neural networks (DNN) excel at extracting patterns. Through
representation learning and automated feature engineering on large datasets,
such models have been highly successful in computer vision and natural language
applications. Designing optimal network architectures from a principled or
rational approach however has been less than successful, with the best
successful approaches utilizing an additional machine learning algorithm to
tune the network hyperparameters. However, in many technical fields, there
exist established domain knowledge and understanding about the subject matter.
In this work, we develop a novel furcated neural network architecture that
utilizes domain knowledge as high-level design principles of the network. We
demonstrate proof-of-concept by developing IL-Net, a furcated network for
predicting the properties of ionic liquids, which is a class of complex
multi-chemicals entities. Compared to existing state-of-the-art approaches, we
show that furcated networks can improve model accuracy by approximately 20-35%,
without using additional labeled data. Lastly, we distill two key design
principles for furcated networks that can be adapted to other domains.