The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.
The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.
For more information please see the following site.
This network is funded by RIKEN-AIP’s subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.
【The 34th Seminar】
Date and Time: Oct. 6th 10:00 am – 11:00 am(JST)
Venue: Zoom webinar
Speaker: Sharon Y. Li (University of Wisconsin Madison)
Title: Challenges and Opportunities in Out-of-distribution Detection
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.
All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.
RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.
|Date||October 6, 2022 (Thu) 10:00 - 11:00|