3:00–4:00 pm Crerar 390, online
December 6, 2021 3:00 PM - 4:00 PM
The University of Chicago Data Science Institute presents a lecture by Gari Clifford, Professor of Biomedical Informatics and Biomedical Engineering at Emory University and the Georgia Institute of Technology, and the Chair of the Department of Biomedical Informatics at Emory.
Abstract: There is a myth in modern machine learning, that as the size of a database increases, and the network depth increases, the performance of an algorithm will continue to improve. This myth is particularly untrue for medical data, which require intense curation to create high-quality labels. As the databases increase in size, the data labels drop in quality or even vanish. Often, the data become noisier with rising levels of non-random missingness. Increasingly, transfer learning is being leveraged to mitigate these problems, allowing algorithms to tune on smaller (or rarer) populations while leveraging information from much larger datasets. I’ll present an emerging paradigm in which we insert an extensive model-generated database in the transfer learning process to help a deep learner explore a much larger and denser data distribution. Since a model allows the generation of realistic data beyond the boundaries of the real data, the model can help train the deep learner to extrapolate beyond the observable collection of samples. Using cardiac time series data, I’ll demonstrate that this technique provides a significant performance boost. I’ll then discuss how general this approach is, and how it can distinguish AI from machine learning.
For more information and to register to view online, visit the event page.