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PublicationInformed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems( 2023)
;Rueden, Laura von ;Walczak, MichalSchuecker, JannisDespite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning. -
PublicationSicherheit von Quantum Machine Learning( 2022-03-24)
;Sultanow, Eldar ;Knopf, ChristianCyberkriminalität bewegt laut Cybersecurity Ventures weltweit schon heute das meiste Geld. Werden Quantencomputer noch dazu beitragen oder die IT-Sicherheit erhöhen? Sie bieten neue Angriffsflächen und können „klassische“ Sicherheitsmechanismen brechen, aber auch die Verteidigung optimieren. Maschinelles Lernen (ML) wird dabei als Quantum Machine Learning (QML) eine wichtige Rolle spielen. -
PublicationAnonymization of German financial documents using neural network-based language models with contextual word representations( 2022-03)
;Loitz, Rüdiger ;Stenzel, RobinThe automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we develop an anonymization method for German financial and legal documents using state-of-the-art natural language processing methods based on recurrent neural nets and transformer architectures. We present a web-based application to anonymize financial documents and a large-scale evaluation of different deep learning techniques.