In this project, students applied techniques from Argument Mining on the new corpus of peer-reviews for scientific publications. Students implemented a pipeline for automatic identification of arguments in peer-reviews and demonstrated empirically the importance of arguments in the decision making process. The work was presented at AAAI-21.
The sun continually emits electrically charged particles. These particles get accelerated/decelerated by the earth’s magnetic field. High-energy particles can pose severe threats to satellite operations and affect electricity plants on the ground. In this project, which is a collaboration with LMU Geophysics, the students developed predictive models for proton intensities in space based on geomagnetic and solar activity indices. The models were applied to investigate the correlation between proton intensities and measurement corruptions of an existing spacecraft and forecast proton intensities to facilitate satellite operators to protect their instruments. The work appeared in the Astrophysical Journal.
Knowledge graphs (KGs) are a way to represent facts in a structured form that machines can efficiently process. There exist several large-scale common knowledge KGs, such as Wikidata or Google Knowledge Graph, but also more specialized ones, for instance, bio-medical ones, such as HetioNet. To combine information from different sources, entities from one graph have to be recognized in the other one, despite potentially additional labels/descriptions / associated data. This task is commonly referred to as Entity Alignment (EA). While humans can easily collect and combine information about an entity from different sources, the task remains challenging for Machine Learning methods.
In this project, the students investigated several state-of-the-art entity alignment methods based on Graph Neural Networks (GNNs) and Generate Adversarial Networks (GANs). They re-implemented the techniques in a common framework, compared the code published by the authors to the method described in the papers, and tried to reproduce the reported results.
Presentation
In the project, we studied the performance of several state-of-the-art argument detection models regarding the generalization capability across multiple argumentation-schemes.
Presentation
In this project, we used data from the KDDCup 2020 to create a realistic taxi-dispatching simulation environment for Reinforcement Learning.
The data was analyzed, cleaned, and used to model the agent’s idle movement within the simulation and the taxi requests of passengers. Different kinds of policies were then implemented and evaluated, e.g., using Kuhn-Munkres and a value-based Reinforcement Learning algorithm.
Presentation
In this project, we applied Multi-agent Reinforcement Learning techniques to teach agents to avoid contact with each other while at the same time trying to get to their target destination as quickly as possible. For that, a flexible grid environment with different agent observations and rewards was implemented. Then, we trained Deep Q-Learning agents to navigate the environments and avoid each other, comparing them to ignorant shortest-path agents as a baseline.
Presentation