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!

Where and When
The AI Reading Group will be held bi-weekly (alternating with ML Student Seminars) on Thursday 2-3pm in 303S-561.
 

Papers
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 06/10/21: Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks

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 work, but the underlying reasons why an attack transfers or not are not yet well understood. In this paper, we present a comprehensive analysis aimed to investigate the transferability of both test-time evasion and...

AI Reading Group 05/27/21: Evaluating Saliency Methods for Neural Language Models

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 interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate...

AI Reading Group 05/13/21: Active Learning Literature Survey

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 instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant or easily obtained, but labels are difficult,...

Jun
24

AI Reading Group

From April 15, 2021 to December 9, 2021, Every 2 weeks at 2:00 pm
Jul
08

AI Reading Group

From April 15, 2021 to December 9, 2021, Every 2 weeks at 2:00 pm
Jul
22

AI Reading Group

From April 15, 2021 to December 9, 2021, Every 2 weeks at 2:00 pm
Aug
05

AI Reading Group

From April 15, 2021 to December 9, 2021, Every 2 weeks at 2:00 pm
Aug
19

AI Reading Group

From April 15, 2021 to December 9, 2021, Every 2 weeks at 2:00 pm

Organisers

Katharina Dost

Katharina Dost

Pat Riddle

Pat Riddle

Archive