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.
AI Reading Group on Nov 25 2021: The neural architecture of language: Integrative modeling converges on predictive processing
Where and when: Thursday, Nov 25 at 2-3pm in Google Meet (see the calendar invite for the link). Abstract The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking...
AI Reading Group on Oct 28 2021: The Computational Gauntlet of Human-Like Learning
Where and when: Thursday, Oct 28 at 2-3pm in 303S-561 or Google Meet (see the calendar invite for the link). Abstract In this paper, we pose a challenge for AI researchers: to develop systems that learn in a human-like manner. We briefly review the history of machine learning, noting that early work made close contact with results from cognitive psychology but that this is no longer the case. We identify seven characteristics of human behavior that, if reproduced, would offer more effective...
AI Reading Group on Oct 14 2021: A Survey of Heterogeneous Information Network Analysis
Where and when: Thursday, Oct 14 at 2-3pm in Google Meet (see the calendar invite for the link) or 303S-561 if possible. Abstract Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous information networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information...