8 citations · 20 across the 6 of their papers we have counts for
6 papers
Translating the future: Image-to-image translation for the prediction of future brain metabolism
Elena Doering, Merle C. Hönig, Tobias Deußer +4
Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading to cognitive decline. [F]-Fluorodeoxyglucose positron emission tomography ([F]-FDG PET) i…
Controlled Randomness Improves the Performance of Transformer Models
Tobias Deußer, Cong Zhao, Wolfgang Krämer +3
During the pre-training step of natural language models, the main objective is to learn a general representation of the pre-training dataset, usually requiring large amounts of tex…
Informed Named Entity Recognition Decoding for Generative Language Models
Tobias Deußer, Lars Hillebrand, Christian Bauckhage +1
Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition…
Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models
Lars Hillebrand, Armin Berger, Tobias Deußer +8
Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text pass…
sustain.AI: a Recommender System to analyze Sustainability Reports
Lars Hillebrand, Maren Pielka, David Leonhard +9
We present sustainAI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies'…
KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
Lars Hillebrand, Tobias Deußer, Tim Dilmaghani +4
We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g.…