4 citations · 9 across the 10 of their papers we have counts for
11 papers
Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation
Tobias Deußer, Abhishek Pillai, Aurelio F. Bariviera +5
Financial institutions operate under dense, frequently amended rulebooks, and answering a compliance question correctly requires not only fluency but verifiable grounding in the au…
On the Impact of Anonymization on the Performance of Large Language Models
Tobias Deußer, Max Hahnbück, Lorenz Sparrenberg +3
As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has become a critical practice. Howev…
Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs
Shahzeb Qamar, Lorenz Sparrenberg, Christian Bauckhage +5
Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely b…
Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation
Priya Tomar, Aditya Parikh, Christian Bauckhage +1
Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relativ…
First Investigation of Deep Learning for Intraoperative Gauze Segmentation in Minimally Invasive Abdominal Surgery
Priya Tomar, Maximilian Broß, Philipp Feodorovici +7
Surgical gauze is an essential part of surgical procedures, primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical retention of gauze can lead to se…
A Survey on Current Trends and Recent Advances in Text Anonymization
Tobias Deußer, Lorenz Sparrenberg, Armin Berger +3
The proliferation of textual data containing sensitive personal information across various domains requires robust anonymization techniques to protect privacy and comply with regul…
Kernel -Medoids as General Vector Quantization
Thore Gerlach, Sascha Mücke, Christian Bauckhage
Vector Quantization (VQ) is a widely used technique in machine learning and data compression, valued for its simplicity and interpretability. Among hard VQ methods, -medoids clu…
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints
Peter Röseler, Oliver Schaudt, Helmut Berg +2
While classical convolutional neural networks (CNNs) have revolutionized image classification, the emergence of quantum computing presents new opportunities for enhancing neural ne…
Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness
Lars Hillebrand, Prabhupad Pradhan, Christian Bauckhage +1
We introduce "pointer-guided segment ordering" (SO), a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations in large…
Model-agnostic Body Part Relevance Assessment for Pedestrian Detection
Maurice Günder, Sneha Banerjee, Rafet Sifa +1
Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability. However, due to the fact that they can only manipulate input to see c…
SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet
Maurice Günder, Facundo Ramón Ispizua Yamati, Abel Andree Barreto Alcántara +3
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine le…