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2020 – today
- 2024
[i10]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)- 2023
- [c6]Timur Sattarov
, Marco Schreyer, Damian Borth:
FinDiff: Diffusion Models for Financial Tabular Data Generation. ICAIF 2023: 64-72 - [c5]Dayananda Herurkar, Timur Sattarov, Jörn Hees, Sebastian Palacio, Federico Raue, Andreas Dengel:
Cross-Domain Transformation for Outlier Detection on Tabular Datasets. IJCNN 2023: 1-8 - [i9]Timur Sattarov, Marco Schreyer, Damian Borth:
FinDiff: Diffusion Models for Financial Tabular Data Generation. CoRR abs/2309.01472 (2023) - 2022
- [c4]Marco Schreyer, Timur Sattarov, Damian Borth:
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. ICAIF 2022: 105-113 - [c3]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 - [i8]Marco Schreyer, Timur Sattarov, Damian Borth:
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. CoRR abs/2208.12708 (2022) - [i7]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) - [i6]Timur Sattarov, Dayananda Herurkar, Jörn Hees:
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data. CoRR abs/2209.10658 (2022) - 2021
- [c2]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 - [i5]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) - 2020
- [c1]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 - [i4]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)
2010 – 2019
- 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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