IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
This work addresses the challenge of leveraging expert knowledge in technical fields to enhance neural network design, offering a domain-specific solution that is incremental in its approach.
The paper tackles the problem of designing optimal neural network architectures by introducing a novel furcated neural network that incorporates domain knowledge as high-level design principles, resulting in a 20-35% improvement in model accuracy for predicting ionic liquid properties compared to state-of-the-art methods without additional labeled data.
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.