by Katharina Dost | Jun 17, 2021 | AI Reading Group, News
Where and when: Thursday, June 24 at 2-3pm in 303S-561 Abstract This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a...
by Katharina Dost | Jun 10, 2021 | ML Student Seminar, News
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...
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...
