In this project, students applied techniques from Emotion Mining on Datasets from the Argument Mining area. Students implemented a pipeline for automatic identification of emotions in arguments and demonstrated empirically the role of pathos in the arguing process.
This team tried to extend a single-agent travelling officer to multiple agents and partial observability (settings where a full sensor network is not available)
This team tried to manage traffic lights on a large scale to optimize traffic in cities.
In this project, which is a cooperation with GFZ, the students work on creating a spatio-temporal model of space weather. In particular, we combine measurements of solar activity measured by satellites and on earth, and geomagnetic activities, and aim to predict several shape parameters of the ionosphere. As a challenge, the time-series do not follow the same cadence, can contain missing values, and the sensors are moving (since they are satellites) - thus we do not have measurements for the same location across time nor for the same time across many locations.
This team investigates different strategies (random, passive, active) to obtain labels from a large pool of unlabeled image data. Given labels after the acquisition, they evaluate the impact of this more or less intelligently selected sparse set on the performance of various models for image classification. More precisely, the students assess the effect of selectively choosing labeled data on a model trained from scratch, a transfer learning model, and a state-of-the-art semi-supervised model that additionally has access to all unlabeled data.
This team worked on one of the datasets from this year’s KDD Cup. The dataset is a full dump of Wikidata, a knowledge graph of 80M entities, 1.3k relation types and ~500M triples. The huge volume poses challenges for training models on GPU. This particularly holds for training graph-neural-network based models which require coherent subgraphs for batching, and efficiently obtaining represenative subgraphs is a non-trivial task.