In this project, the goal was to find anomalies in X-Ray images in a completely unsupervised fashion,
without using labeled data. This project was done in cooperation with Deepc, a start-up company founded
by LMU students.
Publication
In this project, the students dealt with spatial interpolation of weather information in mountain regions.
Our industry partner provided us with data from different regions. One of the main challenges in the project
is to learn a model that can be applied to new regions without the need for re-training.
Publication
The students participated in a real Data Science challenge, where they had to compete against 800 Teams.
The goal of the challenge is to determine what is the best route for the user from different variants proposed
by a transportation app.
Poster,
Presentation
Knowledge graphs are a versatile tool to represent structured data used, for example, in the Wikidata
project or for the Google Knowledge Graph. Link prediction aims at predicting missing links in order to
enrich the knowledge base. This project’s focus is on combining different models into an ensemble in order
to exploit the individual models’ strengths.
Poster,
Presentation
In object detection challenges, neural architectures often fail to detect small objects in images.
Through the recent developments in the research area of super-resolution, it is now possible to improve
quality of images. The students apply recently introduced Super Resolution techniques to improve object
detection performance.
Publication
Argument mining is one of the hardest problems in Natural Language Processing. The main challenge is
that arguments are structurally similar to purely informative texts, and only differ semantically.
In this project, students utilized background information from knowledge graphs for better argument mining.
Poster,
Presentation
Aerial images yield a cost-efficient way to automatically generate census information about the
biodiversity in urban environments. In this project, the students developed a neural-network based object
detection and recognition method for registering and classifying trees in the city of Edmonton.
Poster,
Presentation