Machine Learning Student Seminars

Do you find yourself wondering what great research is being done in our group? Our ML Student Seminar series is intended to give us all a chance to see and hear about what others work on and thereby gives the chance to identify where collaboration is possible. For the students, this is a great chance to present their work and get some feedback and ideas.

In every session, a PhD (or postgrad) student will give a talk on her current work. We will then give feedback, discuss potential problems the student is facing, provide ideas on how to solve challenges and let our creativity flow. The talks are not intended to be perfect and could be quite short. However, the student needs to provide us all with enough information to understand the research and the particular problem she is aiming to solve.

Where and when?

The ML Student Seminars will be held bi-weekly (alternating with the AI Reading Group!) on Thursdays 2pm-3pm in 303S-561. We strongly encourage all members of our group to participate – students and lecturers.

Speakers

In order to have an interesting seminar series, we aim for diverse talks and therefore iterate through supervisors. The supervisors will be asked to nominate students that should give a talk soon — preferably new students that still search for their path, students struggling with a specific problem, but also students that already have a lot of interesting research to report. Please avoid nominating students that just had their Provisional Year Reviews as many of us will already have seen their current work.  Since we meet bi-weekly, every student will be presenting about once a year.

Next ML Student Seminar on June 17 2021: Defining Explainability Via Mental Models

Where and when: Thursday, June 17 at 2-3pm in 303S-561 Speaker: Mike Merry, PhD student, supervised by Pat Riddle, and Jim Warren Abstract: Wide-ranging concerns exist regarding the use of black-box artificial intelligence (AI) in sensitive contexts. Despite performance gains and substantial hype, uptake of AI is still hindered by these concerns. The ability to explain the inner workings of a...

ML Student Seminar June 3 2021: Data has shape and shape has meaning

Where and when: Thursday, June 3 at 2-3pm in 303S-561 Speaker: Neset Tan, PhD student, supervised by Mark Gahegan and Michael Witbrock Abstract: Geometric and topological tools can be effective for understanding complex data. Topological data analysis is a young and growing area that provides strong tools from a combination of algebraic topology and statistics to capture useful patterns in...

ML Student Seminar May 20 2021: Multivariate Sequential Analytics for Long-term Chronic Condition Management

Speaker: William Hsu, PhD student, supervised by Jim Warren and Pat Riddle Abstract: Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the population and is the leading cause of death. An enduring challenge in population health is to accurately identify at-risk...

ML Student Seminar May 6 2021: From Symbolic Logic Reasoning to Soft Reasoning: A Neural-Symbolic Paradigm

Speaker: Qiming Bao, PhD student, supervised by Jiamou Liu and Michael Witbrock Abstract: Combining deep learning with symbolic reasoning aims to capitalize on the success of both fields and is drawing increasing attention. However, it is yet unknown how much symbolic reasoning can be acquired through the means of end-to-end neural networks. In this paper, we explore the possibility of...

ML Student Seminar Apr 22 2021: Domain Adaptation and Bias Mitigation for Regional Varieties of Languages

Speaker: Annie Lu, PhD student, supervised by Yun Sing Koh, and Joerg Wicker Abstract: Regional varieties of languages such as dialects have proved to have different syntactic and semantic features in the linguistics discipline. However, these dialects have low representation in the conventional corpus training dataset for large-size language models. Therefore, direct use of word embeddings...

ML Student Seminar Apr 8 2021: A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method

Speaker: Nooriyan Poonawala-Lohani, PhD student, supervised by Mehnaz Adnan, Pat Riddle, and Joerg Wicker Abstract: Influenza is a communicable respiratory illness that can cause serious public health hazards. Due to its huge threat to the community, accurate forecasting of Influenza-like-illness (ILI) can diminish the impact of an influenza season by enabling early public health...

ML Student Seminar Mar 25 2021: Multi-hop reasoning for QA, based on Knowledge and Information Fusion in Graph Mask Networks

Speaker: Zhenyun Deng, PhD student, supervised by Michael Wittbrock, and Pat Riddle Abstract: Multi-hop question has become an important challenge in reading comprehension (RC) because it requires integrating information from scattered texts across multiple paragraphs. In this paper, we propose a Knowledge and Information Fusion Graph Mask Network (KIF-GMN) to perform reasoning for multi-hop...

Jun
17

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Jul
01

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Jul
15

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Jul
29

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Aug
12

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm

Organization

Katharina Dost

Katharina Dost