NICOP - Causal feature learning
Abstract
Machine learning methods including Deep Learning for feature learning have recently been successful in many applications. Nevertheless, prediction results by Deep Learning approaches are often difficult to understand. It is difficult to see which features actually contribute its prediction performance.We aim to develop data-analytic methods to understand the mechanisms of such predictive machine learning models from the viewpoint of causality, combining the ideas of feature learning and causal structure learning. This is quite useful for users to interpret the prediction results a lot and to make better decisions.We would publish journal papers and make conference presentations to present research results. We are also planning to implement our methods and distribute the codes on the web.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 03, 2017
- Source ID
- N629091712034
Entities
People
- Shohei Shimizu
Organizations
- Office of Naval Research
- Shiga University
- United States Navy