1.6k citations · 2.2k across the 44 of their papers we have counts for
6 papers · 1 filter
Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks
Marco Schreyer, Chistian Schulze, Damian Borth
Nowadays, organizations collect vast quantities of sensitive information in `Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master…
Characterization of Industrial Smoke Plumes from Remote Sensing Data
Michael Mommert, Mario Sigel, Marcel Neuhausler +2
The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of the…
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement
Hamed Hemati, Damian Borth
Recent neural Text-to-Speech (TTS) models have been shown to perform very well when enough data is available. However, fine-tuning them for new speakers or languages is not straigh…
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks
Marco Schreyer, Timur Sattarov, Anita Gierbl +2
The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'. Internationa…
Facial Recognition: A cross-national Survey on Public Acceptance, Privacy, and Discrimination
Léa Steinacker, Miriam Meckel, Genia Kostka +1
With rapid advances in machine learning (ML), more of this technology is being deployed into the real world interacting with us and our environment. One of the most widely applied…
An Investigation of the Weight Space to Monitor the Training Progress of Neural Networks
Konstantin Schürholt, Damian Borth
Safe use of Deep Neural Networks (DNNs) requires careful testing. However, deployed models are often trained further to improve in performance. As rigorous testing and evaluation i…