Responsible and Trusted AI: An Interdisciplinary Perspective

Sun, 01 Dec 2024 00:00:00 +0000

Transparent Transparency Developing a Scheme for Understanding Transparency Requirements

Sun, 01 Dec 2024 00:00:00 +0000

Deep Reinforcement Learning

Sat, 01 Oct 2022 00:00:00 +0000

For details, have a look at the CMS.

Glyph-Based Visual Analysis of Q-Learning Based Action Policy Ensembles on Racetrack

Sat, 01 Jan 2022 00:00:00 +0000

Exploring Graph Neural Networks

Mon, 19 Apr 2021 00:00:00 +0000

Organisation: Prof. Dr. Verena Wolf, Gerrit Großmann.

Please contact Gerrit Großmann for questions about the seminar and have [GNNSeminar] in the subject line.

Registration

Please use the seminar assignment system to register.

Topic

Graphs and networks are ubiquitous in nature, and graph-structured data naturally occurs in a variety of disciplines including physics, chemistry, neuroscience, biology, social science, and epidemiology.

This seminar addresses the problem of learning from graph-structured data. Traditional machine learning and deep learning techniques are not well suited to process data containing rich relational information. Graph Neural Networks (GNNs), Graph Machine Learning, and Geometric Deep Learning offer an interesting perspective on this challenge and are currently one of the fastest-growing topics in ML research. GNNs can assist in discovering drugs, fighting cancer, detecting neurological disorders, analyzing complex systems, recommending friendships in online social networks, controlling traffic, as well as studying epidemiological processes.

We examine recent developments in graph ML research, study state-of-the-art techniques, discuss their limitations, and explore various applications.

A neat overview of GNNs is given in the Graph Neural Networks with Petar Veličković talk.

Organisation

This seminar is a block seminar and will be given on September 23 and 24, 2021, starting at 10:00 sharp.
An online kick-off meeting will be held on Monday, April 19, at 2 pm (1 hour max).
The seminar language is English.
The seminar earns you 7 ECTS.
The seminar is eligible for bachelor and master students of computer science.
Send three topic preferences (unordered) to Gerrit by Monday, April 26.
Depending on the study regulations, registration/unregistration in HISPOS/LSF is due by Tuesday May 18.
Mock presentation deadline: Friday, Sep 10.
Short reports deadline: Sunday, Oct 24 (by email).

Requirements

A basic understanding of machine learning and deep learning will be helpful but is not mandatory. If you are new to the field, the 3blue1brown Neural Network Playlist might be a good start. For further consultation of general concepts, see the Deep Learning Book by Goodfellow et al. (available online). Additional material on GNNs can be found in the Graph Representation Learning Book by Hamilton (available online). There are many more good introductory GNN and ML talks on YouTube.

Grade

To pass, you have to attend all sessions, give a presentation, participate in discussions, and submit reports. Furthermore, we expect all participants to read all seminar papers in the course of the semester and write down questions/thoughts/ideas/etc. (of course, we do not expect you to understand them fully). Each participant must also give an ungraded mock presentation to their group (A, B, or C) at least 2 weeks prior to the actual presentation.

The final grade consists of: your presentation (40%), reports (40%), and participation during the seminar discussions (20%).

Presentation

Identify the key ideas and concepts and give a self-consistent presentation explaining these concepts to your fellow students. The presentation should focus on intuition and high-level understanding and contextualization, not on technical details. We encourage you to read related work and explore supplementary material and include it in the presentation where it seems useful (e.g., from related literature, YouTube, GitHub, Medium articles, OpenReview, etc.). The presentation should be 20 to 30 minutes long.

Report

Each student has to submit four short reports (one for each paper of their group, 1000–2000 words). The report should contain a short summary of (what you consider to be) the main contribution or most intriguing idea of the paper. Otherwise, you can freely express your own thoughts on the topic. For instance: What did you like/dislike about the paper (both methodically and didactically)? What are connections to other seminar papers? Can you suggest improvements? What do you think is missing? Can you think of other useful applications?

You can use the Springer LNCS LaTeX template (every other reasonable formatting is fine, too).

Discussions

Read each presented paper in advance (of course, we do not expect you to understand them fully) and actively participate in the discussion. We expect each participant to attend each session.

Topics

Foundations

Date	Timeslot	Student	Topic	Group
23.09.2021	10:00	Safya Alzayat	Semi-Supervised Classification with Graph Convolutional Networks	A
23.09.2021	10:45	Suruthai Noon Goldstein	Inductive Representation Learning on Large Graphs	B
23.09.2021	–	Siwen Chen	Neural Message Passing for Quantum Chemistry	C

Applications

Date	Timeslot	Student	Topic	Group
23.09.2021	11:30	Gilbert El Khoury	BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis	A
23.09.2021	13:00	Divya Nidadavolu	Fake News Detection on Social Media using Geometric Deep Learning	B
23.09.2021	13:45	Roman Joeres	Modeling polypharmacy side effects with graph convolutional networks	C
24.09.2021	10:00	Maryam Meghdadi Esfahani	Neural Relational Inference for Interacting Systems	A
24.09.2021	10:45	Yan Yan Lau	Finding Patient Zero: Learning Contagion Source with Graph Neural Networks	B
24.09.2021	11:30	Aakash Rajpal	MolGAN: An implicit generative model for small molecular graphs	C

Properties, limitations, and recent advances

Date	Timeslot	Student	Topic	Group
24.09.2021	12:45	Adarsh Jamadandi	How Powerful are Graph Neural Networks?	A
24.09.2021	13:30	Vabuk Pahari	Strategies for Pre-training Graph Neural Networks	B
24.09.2021	14:15	Zhifei Li	Adversarial Attacks on Neural Networks for Graph Data	C

The participants are split into three groups (A, B, C — with four participants each). Each student writes four reports, one for each paper of their group. The mock presentations are also given within these groups.

Deep Reinforcement Learning

Thu, 01 Apr 2021 00:00:00 +0000

Team

Prof. Dr. Verena Wolf
Timo P. Gros, Joschka Groß (assistants)

The course pages of this seminar were on the group’s former CMS, which is no longer online. The seminar ran again in Winter 2022/23.

Deep Learning | Neuro-Mechanistic Modeling

Responsible and Trusted AI: An Interdisciplinary Perspective

Transparent Transparency Developing a Scheme for Understanding Transparency Requirements

Deep Reinforcement Learning

Glyph-Based Visual Analysis of Q-Learning Based Action Policy Ensembles on Racetrack

Exploring Graph Neural Networks

Registration

Topic

Organisation

Requirements

Grade

Presentation

Report

Discussions

Topics

Deep Reinforcement Learning

Team