  The scientific community has been increasingly interested in harnessing the
power of deep learning to solve various domain challenges. However, despite the
effectiveness in building predictive models, fundamental challenges exist in
extracting actionable knowledge from deep neural networks due to their opaque
nature. In this work, we propose techniques for exploring the behavior of deep
learning models by injecting domain-specific actionable attributes as tunable
"knobs" in the analysis pipeline. By incorporating the domain knowledge in a
generative modeling framework, we are not only able to better understand the
behavior of these black-box models, but also provide scientists with actionable
insights that can potentially lead to fundamental discoveries.
