Congrats to Colin for successfully defending his Ph.D. thesis.
Machine learning, and the sub-field of deep learning in particular, has experienced an explosion in research interest and practical applications over the past few decades. Deep learning approaches seem to have become the preferred approach in many domains, outpacing the use of more traditional machine learning methods. This transition has also coincided with a shift away from feature engineering based on domain knowledge. Instead, the common deep learning philosophy is to learn relevant features through the combination of expressive models and large datasets. Some have interpreted this paradigm shift as the death of domain knowledge. I argue that domain knowledge is still broadly used in deep learning systems, and even critically important, but where and how domain knowledge is used has evolved. To support this argument I present three recent deep learning applications in disparate domains that each heavily rely on domain knowledge. Based on these three applications I discuss strategies for where and how domain knowledge is being effectively incorporated into newer deep learning systems.
Defense Date: June 14th, 2021
Dissertation Examination Committee: Safwan Wshah, Ph.D., Advisor
Jianing Li, Ph.D., Chairperson
Nicholas Cheney, Ph.D.
Christopher Danforth, Ph.D.
Brian Tivnan, Ph.D.