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.
Recommended previous knowledge
- B.Inf.1236 Machine Learning
- B.Inf.1237 Deep Learning (the seminar can accompany lecture in the same term)
- 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
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.
|2021/10/26||Introductory session||Timo, Richard, Laura|
|2021/11/23||Vision Transformers||Richard||An Image is Worth 16x16 Words||Silin|
|2021/11/30||Recent large-scale models||Timo||Language Models are Few-Shot Learners||Lindrit|
|2021/11/30||Recent large-scale models||Timo||Learning Transferable Visual Models From Natural Language Supervision||Abdullah|
|2021/12/7||Self-supervised learning||Timo||Exploring Simple Siamese Representation Learning||Sharmita|
|2021/12/14||Implicit shape models||Laura||Deep Implicit Templates||Setareh|
|2021/12/14||Graph neural networks||Laura||GraphSAGE||Lucky|
|2022/2/28||Submission of final report|
Papers to be discussed
- Vision Transformer [Richard]
- Recent large-scale models [Timo]
- Self-supervised learning [Timo]
- Tracking [Richard]
- Implicit shape models [Laura]
- Graph neural networks [Laura]