Timur Sattarov

Orcid: 0000-0003-0460-0313

According to our database1, Timur Sattarov authored at least 13 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Bibliography

2024
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation.
CoRR, 2024

Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular Data.
Proceedings of the 5th ACM International Conference on AI in Finance, 2024

2023
Cross-Domain Transformation for Outlier Detection on Tabular Datasets.
Proceedings of the International Joint Conference on Neural Networks, 2023

FinDiff: Diffusion Models for Financial Tabular Data Generation.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023

2022
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data.
CoRR, 2022

Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

2021
Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks.
Proceedings of the ICAIF'21: 2nd ACM International Conference on AI in Finance, Virtual Event, November 3, 2021

2020
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks.
CoRR, 2020

Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks.
Proceedings of the ICAIF '20: The First ACM International Conference on AI in Finance, 2020

2019
Adversarial Learning of Deepfakes in Accounting.
CoRR, 2019

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks.
CoRR, 2019

2017
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks.
CoRR, 2017


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