Below, Eileen is in conversation with Dr. Elizabeth Lorang, Dr. Leen-Kiat Soh, and doctoral candidates Mike Pack and Yi Liu. They are members of a research team from the University of Nebraska-Lincoln collaborating with the Library of Congress on applying machine learning algorithms to Library collections for processing, metadata generation, and enhancing discoverability.
What, in your opinion, was the most promising outcome of the five machine learning projects you worked on this summer?
Soh: To me, [it] was the explorative nature of the five projects informed by insights from analyzing the data and by hands-on practical concerns from the Library.
Pack: I was excited about the fact that a set of features extracted by a deep learning model could deal with several tasks, such as classification and segmentation. Also, the fact that transfer learning (i.e., knowledge transfer) reduces training time makes it worthwhile to delve further into what deep representation can do, such as clustering document images.
Liu: