Wednesday / CS Seminar

June 4th / 12:00 PM

Hybrid / Mudd 3514

Speaker: George K. Thiruvathukal

Talk Title: Reusing Deep Learning Models: Challenges and Directions in Software Engineering: An Update

Abstract: Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Re-using DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in re-using DNNs. These challenges include both missing technical capabilities and missing engineering practices. We discusschallenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, including conceptual (e.g., re-using based on a research paper), adaptation (e.g., re-using by building on an existing implementation), and deployment (e.g., direct re-use on a new device). We outline possible advances that would improve each kind of re-use.

In this update of my invited presentation at IEEE Services 2023 at the John Vincent Atanasoff Sypmposium on Modern Computing, I will include information about our work in progress on the use of Deep Learning and Pre-Trained Models (PTMs) in science and engineering applications and our efforts to conduct automated longitudinal analysis of same.

Biography: https://gkt.sh/about/

Zoom: https://northwestern.zoom.us/j/95711534679?pwd=CWRzR6h29LB4a1SxIsBP8Zrjepwfqw.1

Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e0252947-0320-499c-acb8-b2ec01126b9c