Description
[The 34th Seminar]
Date and Time: Oct. 6th 10:00 am – 11:00 am(JST)
Venue: Zoom webinar
Language: English
Speaker: Sharon Y. Li (University of Wisconsin Madison)
Title: Challenges and Opportunities in Out-of-distribution Detection
Short Abstract:
The real world is open and full of unknowns, presenting significant challenges for machine learning (ML) systems that must reliably handle diverse, and sometimes anomalous inputs. Out-of-distribution (OOD) uncertainty arises when a machine learning model sees a test-time input that differs from its training data, and thus should not be predicted by the model. As ML is used for more safety-critical domains, the abilities to handle out-of-distribution data are central in building open-world learning systems. In this talk, I will talk about challenges, research progress and and future opportunities in detecting OOD samples for safe and reliable predictions in an open world.
