Current Topics in Deep Learning

Current Topics in Deep Learning

In this seminar recent deep learning papers will be presented and discussed. The goal is to dive deeper into deep learning topics beyond what can be covered in a lecture and also to give an insight in recent research topics in our lab. To name some of the topics: vision transformer and other recent large-scale models, implicit shape models and multi-object tracking. Below there is a list of papers of which you can choose a paper you want to present. It is also possible to suggest another cool paper in one of the topics.

  • B.Inf.1236 Machine Learning
  • B.Inf.1237 Deep Learning (the seminar can accompany lecture in the same term)

Examination

  • Oral presentation (approx. 30 min.) and term paper (max. 5000 words)
  • Examination requirements
    • Knowledge in a specific field of machine learning
    • Ability to present the acquired knowledge in a both orally and in a written report

Organization

There will be a starting session where papers are assigned to students. In the next weeks, in each weekly session, two papers will be presented (20 minutes) and subsequently discussed (10 to 20 minutes).

Our plan is to hold this seminar in presence in room IfI 2.101 on Tuesdays 12:00 - 14:00.

If you are interested in this course, please register at Stud.IP.

Schedule

Date Topic Supervisor Paper Student
2021/10/26 Introductory session Timo, Richard, Laura
2021/11/2 -
2021/11/9 -
2021/11/16 Vision Transformers Richard
2021/11/23 Recent large-scale models Timo
2021/11/30 Self-supervised learning Timo
2021/12/7 Tracking Richard
2021/12/14 Implicit shape models Laura
2021/12/21 Graph neural networks Laura
2022/1/11 Open topic
2022/1/18 -
2022/1/25 -
2022/2/1 -
2022/2/28 Submission of final report

Papers to be discussed

Neural Data Science Group
Institute of Computer Science
University of Goettingen