AI Reading Group
The AI Reading Group hosts bi-weekly reading groups where members present and discuss papers on topics in broad categories, such as AI ethics, machine learning, natural language processing, selection bias and computer vision. Come and join us in the discussion of ideas and seminal papers in AI Research to understand current developments and debates in the field!
The AI Reading Group will be held bi-weekly (alternating with ML Student Seminars) on Thursday 2-3pm in 303S-561.
Papers will be selected by alternating members of the group and the paper schedule will be announced two weeks in advance. We then encourage everyone to read the paper before joining the session. During each session, members will be discussing strengths/weaknesses/impact/novelty of the paper.
While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust...
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures, like the dependency tree of sentences and the scene graph of images, is an important research topic which...