MLT __init__ is a monthly event led by Jayson Cunanan and J. Miguel Valverde where, similarly to a traditional journal club, a paper is first presented by a volunteer and then discussed among all attendees. Our goal is to give participants good initializations to effectively study and improve their understanding of Deep Learning. We will try to achieve this by:
- Discussing fundamental papers, whose main ideas are currently implemented on state-of-the-art models.
- Discussing recent papers.
Sessions will be held via Zoom starting at 5pm (JST) / 10am (CET). Check at what time is in your region here.
MLT __init__ is worldwide! On average we had around 30-50 participants in each of our sessions, joining from, at least, 42 countries. The top 5 countries with the highest number of participants were Japan 🇯🇵, India 🇮🇳, United Kingdom 🇬🇧, Germany 🇩🇪, and Finland 🇫🇮. Thank you for being part of MLT __init__ 🤗
Introduction (5min) + Paper presentation (25min) + Discussion (30min)
We record the introduction and the presentation but not the discussion, allowing participants to interact while protecting their privacy.
We kindly ask participants to read the paper in advance and to join the session with questions and comments. These questions/comments can be to highlight interesting or unclear parts. For instance: what did you like the most about this paper? What did you learn? What did you not understand?
To make the session more interactive, participants can also ask questions during the presentation. We encourage everyone to use their microphone, but please keep in mind the environmental noise. If you cannot use your microphone or you want to keep your privacy, you are welcome to write in the Zoom chat or Slack channel, and either Jayson or Miguel will read your questions aloud.
Presenters will prepare a Powerpoint/Keynote presentation that will be shared in this repository after the session. The presentation should last around 25 mins so that there is enough time for questions and discussion. Inline with the goals of MLT __init__, we encourage presenters to incorporate intuitive visualizations, code, Jupyter notebooks, Colab, and any other material. Finally, please keep in mind that MLT __init__ audience has a very heterogeneous background.Some ideas for the presentation:
- Background knowledge required to understand the paper.
- Motivation of the paper, what is the problem that authors try to solve?
- Contributions of the paper.
As this event aims to be interactive, please remember to be kind and respectful to each other. Full code of conduct here.
Feedback and contact form: https://forms.gle/jJLWyAMjjVKL8KFRA