Events

Filters
Export this event to calendar
iCal
RSS
RSS
Close X

Filter by:

Limit to events where the first date of the event:
Date range
Limit to events where the first date of the event:
Limit to events where the title matches:
Limit to events where the type is one or more of:
Limit to events tagged with one or more of:
Limit to events where the audience is one or more of:
Monday, July 10, 2023 1:00 pm - 2:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Human-Computer Interaction • Eggly: Designing Mobile Augmented Reality Neurofeedback Training Games for Children with Autism Spectrum Disorder

Please note: This master’s thesis presentation will take place in DC 1331 and online.

Yue Lyu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Jian Zhao, Keiko Katsuragawa

Thursday, July 13, 2023 9:00 pm - 11:59 pm EDT (GMT -04:00)

PhD Defence • Software Engineering | Software Testing • Assessing the Reliability of Deep Learning Applications

Please note: This PhD defence will take place online. Also note the atypical start time — 9:00 p.m.

Yongqiang Tian, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Chengnian Sun

Friday, July 14, 2023 12:00 pm - 1:00 pm EDT (GMT -04:00)

Seminar • Algorithms and Complexity • Causal Structure Learning and Online Algorithms with ML Advice

Please note: This seminar will take place in DC 1304 and online.

Themis Gouleakis, Senior Research Fellow
National University of Singapore

The enormous success of the field of machine learning in recent years and its ability to make accurate predictions using data has also influenced research in other areas. In this talk, we will explore such settings where “machine learned advice” can be exploited.

Tuesday, July 18, 2023 3:00 pm - 6:00 pm EDT (GMT -04:00)

PhD Defence • Machine Learning | Natural Language Processing • Less is More: Restricted Representations for Better Interpretability and Generalizability

Please note: This PhD defence will take place online.

Zhiying (Gin) Jiang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

In this thesis, we aim at improving interpretability and generalizability through restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in low-data regime.