Projects

Project Manager: Christian Jaumann

Principal Investigators: Prof. Dr. Annemarie Friedrich

Affiliation: University Augsburg

HPC Platform used: NHR@FAU: Alex GPU cluster

The scientific literature is growing rapidly on a daily basis, making it hard to keep track of the state-of-the-art. Systematic literature reviews (SLRs) aim to identify and evaluate all relevant literature on a topic. After retrieving a set of candidate papers, the abstract screening phase determines initial relevance according to a set of criteria. Large Language Models (LLMs) provide strong text interpretation skills, but are not naturally suited for creating ranked lists of large sets of documents due to scalability issues. We combine LLM-based relevance judgments with neural dense re-rankers for abstract screening, outperforming existing ranking systems on the task by a large margin.

Principal Investigator: Prof. Dr. Josif Grabocka

Affiliation: University of Technology Nuremberg

HPC Platform Used: Alex GPU cluster (NHR@FAU)

Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this project, we developed a new class of interpretable neural networks for tabular data that are both deep and linear at the same time (i.e. mesomorphic). We optimize deep hypernetworks to generate explainable linear models on a per-instance basis. As a result, our models retain the accuracy of black-box deep networks while offering free-lunch explainability for tabular data by design. Through extensive experiments, we demonstrate that our explainable deep networks have comparable performance to state-of-the-art classifiers on tabular data and outperform current existing methods that are explainable by design.

Find the paper here.

Project Manager: Johannes Schusterbauer-Fischer

Principal Investigator: Prof. Dr. Björn Ommer

Affiliation: Ludwig Maximilian University Munich (LMU Munich)

HPC Platform Used: Alex GPU cluster (NHR@FAU)

Visual synthesis has recently seen significant leaps in performance, largely due to breakthroughs in generative models. Diffusion models have been a key enabler, as they excel in image diversity. However, this comes at the cost of slow training and synthesis, which is only partially alleviated by latent diffusion. To this end, flow matching is an appealing approach due to its complementary characteristics of faster training and inference but less diverse synthesis. We demonstrate that introducing flow matching between a frozen diffusion model and a convolutional decoder enables high-resolution image synthesis at reduced computational cost and model size. A small diffusion model can then effectively provide the necessary visual diversity, while flow matching efficiently enhances resolution and detail by mapping the small to a high-dimensional latent space. These latents are then projected to high-resolution images by the subsequent convolutional decoder of the latent diffusion approach. Combining the diversity of diffusion models, the efficiency of flow matching, and the effectiveness of convolutional decoders, state-of-the-art high-resolution image synthesis is achieved at 1K and 2K resolution with minimal computational cost. Importantly, our approach is orthogonal to recent approximation and speed-up strategies for the underlying model, making it easily integrable into the various diffusion model frameworks.

Find more information and the paper here.

Principal Investigator: Prof. Dr. Josif Grabocka

Affiliation: University of Technology Nuremberg

HPC Platform Used: Alex GPU cluster (NHR@FAU)

Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this project, we developed a new class of interpretable neural networks for tabular data that are both deep and linear at the same time (i.e. mesomorphic). We optimize deep hypernetworks to generate explainable linear models on a per-instance basis. As a result, our models retain the accuracy of black-box deep networks while offering free-lunch explainability for tabular data by design. Through extensive experiments, we demonstrate that our explainable deep networks have comparable performance to state-of-the-art classifiers on tabular data and outperform current existing methods that are explainable by design.

Find the paper here.

Project Manager: Jan Pfister, Julia Wunderle 

Principal Investigators: Prof. Dr. Andreas Hotho

Affiliation: Julius-Maximilians University Of Würzburg (JMU)

HPC Platform used: Alex GPU cluster (NHR@FAU)

We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to use. The model training involved several key steps, including extensive data preprocessing, the creation of a custom German tokenizer, the training itself, as well as the evaluation of the final models on various benchmarks. Throughout the training process, multiple checkpoints were saved and analyzed using the SuperGLEBer benchmark to monitor the models’ learning dynamics. Compared to state-of-the-art models on the SuperGLEBer benchmark, both LLäMmlein models performed competitively, consistently matching or surpassing models with similar parameter sizes. The results show that the models’ quality scales with size as expected, but performance improvements on some tasks plateaued early, offering valuable insights into resource allocation for future model development.

Find the paper here.

Project Managers: Katharina Winter & Erik Schütz

Principal Investigator: Prof. Dr. Fabian Flohr

Affiliation: University of Applied Sciences Munich (HM)

HPC Platform Used: Alex GPU cluster (NHR@FAU)

Generative models are increasingly used in Autonomous Driving applications to improve decision-making, safety, and generalization to unseen scenarios and corner cases. At the Intelligent Vehicles Lab, we focus on Encoder-Decoder architectures and Large Language Models to enhance prediction and planning in urban driving environments and simulations. Powered by NHR, our research develops solutions that help autonomous systems navigate complex, real-world situations while making Autonomous Driving more explainable, ensuring trustworthiness and interpretability. As part of the nxtAIM project, a nationwide initiative focusing on Generative AI for Autonomous Driving, we contribute to shaping the future of AI in Germany.

Find current publications here.