by Katharina Dost | May 28, 2021 | Adversarial Learning, AI Reading Group, News
Where and when: Thursday, June 10 at 2-3pm in 303S-561 Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous...
by Katharina Dost | May 27, 2021 | ML Student Seminar, News
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...
by Katharina Dost | May 10, 2021 | AI Reading Group, News
Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these...
by Katharina Dost | May 6, 2021 | Computational Sustainability, ML Student Seminar, News
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...
by Katharina Dost | Apr 29, 2021 | AI Reading Group, News
The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data...
by Katharina Dost | Apr 27, 2021 | ML Student Seminar, News
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...
