ELLIS Reading Groups
As part of its educational activities, ELLIS aims to connect PhDs and postdocs in the network and supports them in creating opportunities to discuss science.
Currently, two student-led reading groups on machine learning-related topics meet regularly to discuss a paper the group previously voted on. ELLIS Reading Groups are initiated and organized by ELLIS PhD students and postdocs across Europe.
1. Human-Centric Machine Learning (HCML)
The HCML reading group aims to gather researchers and students interested in both getting a wide vision of the topic and also deeply diving into it. Reading papers about different topics inside HCML, and also discussing new problem set-ups, different approaches, and sources of bias will lead us to a broad understanding of how algorithmic and human decisions influence each other.
Chairs: Adrián Arnaiz-Rodríguez, Aditya Gulati, Gergely Németh, Piera Riccio
Next session:
Title: What is Beautiful is Still Good: The Attractiveness Halo Effect in the era of Beauty Filters
Authors: Aditya Gulati, Marina Martinez-Garcia, Daniel Fernandez, Miguel Angel Lozano, Bruno Lepri, Nuria Oliver
Abstract: The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images from a diverse set of 462 distinct individuals in two conditions: original and attractive after applying a beauty filter.
Our study reveals that the same individuals receive statistically significantly higher ratings of attractiveness and other traits, such as intelligence and trustworthiness, in the attractive condition. We also study the impact of age, gender, and ethnicity and identify a weakening of the halo effect in the beautified condition, resolving conflicting findings from the literature and suggesting that filters could mitigate this cognitive bias. Finally, our findings raise ethical concerns regarding the use of beauty filters.
Paper link: https://doi.org/10.21203/rs.3.rs-4698268/v1
Presenter: Aditya Gulati
When: 30th of July at 3pm CEST
An overview of past and upcoming sessions is provided on the group's webpage.
Join the Google Group here.
2. Mathematics of Deep Learning
The Mathematics of Deep Learning group is for discussing the theoretical background of the state-of-the-art deep neural networks. This theory is aiming at explaining the mechanisms behind the training, generalization, architecture, initialization choosing, etc. This reading group is not restricted by any particular application, so various tasks can be considered, ranging from the purely theoretical linear networks to (current) state-of-the-art transformer networks for natural language processing. The group’s main goal is to keep up with the most advanced areas of research in understanding mathematics behind deep neural networks and their optimization.
Chairs: Linara Adilova (Ruhr University Bochum), Oishi Deb (University of Oxford), Sidak Pal Singh (ETH Zurich)
Next sessions:
Data/Time: Thur, 30th of May,18:00 CET (17:00 UK time)
Topic: On a continuous time model of gradient descent dynamics and instability in deep learning
Presenter: Mihaela Rosca
Affiliation: Google DeepMind
Find more information about the group's sessions on this website.
Join the Google Group here.
Who can join ELLIS Reading Groups?
ELLIS Reading Groups are open to any PhD and postdoc students interested in discussing the respective topics with others. New members are always welcome! To join a group, please submit a request via the Google Groups. For any further questions, please contact phd@ellis.eu.
More information
Find more information about the ELLIS PhD Program here. This program has received funding from the European Union’s Horizon 2020 research and innovation programme under ELISE Grant Agreement No. 951847.
Social media
Stay up to date on the latest activities and follow ELLIS on Twitter, LinkedIn, Mastodon and Facebook!