Modern language models show impressive performance across a wide range of domains and tasks. Despite this often impressive performance, it is largely unknown how their general-purpose components such as transformers concretely achieve these goals. This talk will discuss established and recently proposed methods to model interpretation and introduce some late-breaking results from the joint lab at the University of Tübingen and Brown University, led by Carsten Eickhoff.
Carsten Eickhoff is a professor of medical data science and computer science at the University of Tübingen where his lab specializes in the development of machine learning and natural language processing techniques with the goal of improving patient safety, individual health and quality of medical care. Prior to joining Tübingen, he was the Manning Assistant Professor of Medical and Computer Science at Brown University. He received degrees from the University of Edinburgh and TU Delft, and was a postdoctoral fellow at ETH Zurich and Harvard University. Eickhoff has authored more than 150 articles in computer science conferences (e.g., ICLR, ACL, SIGIR, WWW, KDD) and clinical journals (e.g., Nature Digital Medicine, The Lancet – Respiratory Medicine, Radiology, European Heart Journal). His research has been supported by the Swiss National Science Foundation, NSF, NIH, DARPA, IARPA, Google, Amazon, Microsoft and others. Aside from his academic endeavors, he is a founder and board member of several deep technology startups in the health sector that strive to translate technological innovation to improved safety and quality of life for patients.
For more information about this seminar, visit datascience.unc.edu.