Intelligent Systems

Seminar - Deep Learning


Seminar (infSemDeLea-01a)

2 SWS, ECTS studies: 5
Suitable for ERASMUS / exchange students, language of instruction: English
Time and place: irregular rotation
Lecture period winter term 2021-22
More information see UnivIS or in Modulinformationssystem


The KickOff event for the Master Seminar "Deep Learning" will take place on Thursday, September 23, 2021 at 2 p.m. as a zoom call. There, the seminar schedule will be explained and topics will be assigned. In preparation for the topic assignment, the starting papers for the individual topics are available. If you are interested in the event, already want to reserve a place or have any questions, please contact Prof. Tomforde directly.


General information

In this seminar important work of the last years in the field of Deep Learning will be discussed. The seminar is offered in cooperation with the "Multimedia Information Processing" workgroup. The main focus is therefore on problems in the field of computer vision and active learning. For example, topics such as object detection, image description, inpainting, segmentation, but also novel learning methods are discussed.

Subject / Topics (Entries that are grayed out are already assigned):

 1. Curling (link)

 2. Soft Actor-Critic (link)

 3. Multi-Agent Actor-Critic (link)

 4. DRL in Communications and Networking (link)

 5. Learning to Walk (link)

 6. DRL for trading (link)

 7. DRL without Exploration (link)

 8. DRL with Natural Language Action Space (link)

 9. A DRL Chatbot (link)

10. Experience-driven networking (link)


We strongly suggest the participation of at least one of the following lectures Inf-NNDL: Neural networks and deep learning, Autonomous Learning, Intelligent Systems.

The assessment in this module consists of the preparation of a written elaboration, the review of two works of your fellow students, as well as a presentation with subsequent discussion.

The complete course will be held in English. The elaboration, reviews and presentation (incl. discussion) will be done in English.

If you have any questions, please contact  Simon Reichhuber


Written elaboration

Your first task is to read and understand the paper assigned to you. You should also consider quoted literature that is necessary for understanding. Then you should summarize the contents of your paper in your own words. We expect about 12-20 pages. Again, you should not limit yourself to your paper, but also include accompanying literature.

The written elaboration is submitted via the EasyChair conference system.



In a review you should assess whether or not the author has fulfilled the task of summarising the given paper. Of course, you also have to consider the original paper. The reviews are also carried out via the EasyChair conference system. You will be assigned two papers, which you should edit as follows:

Summary: Here you should briefly summarize the content of the elaboration. This shows that you as a reviewer have understood what it is all about.

Paper strength: Here you should list what you liked about the elaboration. So if you had to defend your colleague, these would be your arguments why the elaboration is good.

Paper weaknesses: Here you should list what you didn't like about the elaboration. So if you had to defend the rejection of the work, those would be your arguments why the elaboration is not good.

Preliminary evaluation: A summary of your opinion with arguments as to whether the task was fulfilled or not.

Overall evaluation: That summarizes again in a grade whether the elaboration has fulfilled the task or not. This recommendation will not be passed on to the author.

The reviews are part of the assessment in this seminar and are therefore included in your grade. We will evaluate how conscientiously you have reviewed the work of your fellow students.



During the presentation, you should give a 20-minute presentation of the contents of the paper assigned to you. Afterwards we would like to have a short discussion about the contributions of the paper to Deep-Learning research. You as the speaker should lead this discussion and, if necessary, start it with questions you have prepared yourself. We plan to hold this mini-conference online.



30 h attendance, 120 h self-study