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Marco Schreyer
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Conference and Workshop Papers
- 2023
- [c9]Timur Sattarov
, Marco Schreyer
, Damian Borth
:
FinDiff: Diffusion Models for Financial Tabular Data Generation. ICAIF 2023: 64-72 - 2022
- [c8]Marco Schreyer
, Timur Sattarov, Damian Borth:
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. ICAIF 2022: 105-113 - [c7]Ricardo Müller, Marco Schreyer
, Timur Sattarov, Damian Borth:
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations. ICAIF 2022: 174-182 - 2021
- [c6]Marco Schreyer
, Timur Sattarov, Damian Borth:
Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks. ICAIF 2021: 8:1-8:8 - 2020
- [c5]Marco Schreyer
, Timur Sattarov, Anita Gierbl, Bernd Reimer, Damian Borth:
Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks. ICAIF 2020: 45:1-45:8 - 2010
- [c4]Joost van Beusekom, Marco Schreyer
, Thomas M. Breuel:
Automatic counterfeit protection system code classification. Media Forensics and Security 2010: 75410F - 2009
- [c3]Christian Schulze, Marco Schreyer, Armin Stahl, Thomas M. Breuel:
Using DCT Features for Printing Technique and Copy Detection. IFIP Int. Conf. Digital Forensics 2009: 95-106 - [c2]Marco Schreyer:
Intelligent Printing Technique Recognition and Photocopy Detection for Forensic Document Examination. Informatiktage 2009: 39-42 - 2008
- [c1]Christian Schulze, Marco Schreyer, Armin Stahl, Thomas M. Breuel:
Evaluation of Graylevel-Features for Printing Technique Classification in High-Throughput Document Management Systems. IWCF 2008: 35-46
Informal and Other Publications
- 2024
- [i13]Timur Sattarov, Marco Schreyer, Damian Borth:
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation. CoRR abs/2401.06263 (2024) - [i12]Kristófer Reynisson, Marco Schreyer, Damian Borth:
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs. CoRR abs/2407.12440 (2024) - 2023
- [i11]Timur Sattarov, Marco Schreyer, Damian Borth:
FinDiff: Diffusion Models for Financial Tabular Data Generation. CoRR abs/2309.01472 (2023) - 2022
- [i10]Marco Schreyer, Timur Sattarov, Damian Borth:
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. CoRR abs/2208.12708 (2022) - [i9]Ricardo Müller, Marco Schreyer, Timur Sattarov, Damian Borth:
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations. CoRR abs/2209.09157 (2022) - [i8]Marco Schreyer
, Hamed Hemati, Damian Borth, Miklos A. Vasarhelyi:
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing. CoRR abs/2210.15051 (2022) - 2021
- [i7]Marco Schreyer, Timur Sattarov, Damian Borth:
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks. CoRR abs/2109.11201 (2021) - [i6]Hamed Hemati, Marco Schreyer, Damian Borth:
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data. CoRR abs/2112.13215 (2021) - 2020
- [i5]Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer, Damian Borth:
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks. CoRR abs/2008.02528 (2020) - [i4]Marco Schreyer, Christian Schulze, Damian Borth:
Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks. CoRR abs/2012.07110 (2020) - 2019
- [i3]Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, Damian Borth:
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks. CoRR abs/1908.00734 (2019) - [i2]Marco Schreyer, Timur Sattarov, Bernd Reimer, Damian Borth:
Adversarial Learning of Deepfakes in Accounting. CoRR abs/1910.03810 (2019) - 2017
- [i1]Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, Bernd Reimer:
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks. CoRR abs/1709.05254 (2017)
Coauthor Index
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