1.6k citations · 2.2k across the 44 of their papers we have counts for
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FinDiff: Diffusion Models for Financial Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy re…
Partial Hypernetworks for Continual Learning
Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu +1
Hypernetworks mitigate forgetting in continual learning (CL) by generating task-dependent weights and penalizing weight changes at a meta-model level. Unfortunately, generating all…
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
Dominik Honegger, Konstantin Schürholt, Damian Borth
With growing size of Neural Networks (NNs), model sparsification to reduce the computational cost and memory demand for model inference has become of vital interest for both resear…
Class-Incremental Learning with Repetition
Hamed Hemati, Andrea Cossu, Antonio Carta +5
Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike r…
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
Marco Schreyer, Hamed Hemati, Damian Borth +1
The International Standards on Auditing require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central ob…
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Schürholt, Diyar Taskiran, Boris Knyazev +2
In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their wei…
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto +1
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architect…
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
Ricardo Müller, Marco Schreyer, Timur Sattarov +1
Detecting accounting anomalies is a recurrent challenge in financial statement audits. Recently, novel methods derived from Deep-Learning (DL) have been proposed to audit the large…
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
Marco Schreyer, Timur Sattarov, Damian Borth
The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors…
Hyper-Representations for Pre-Training and Transfer Learning
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto +1
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architect…
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
Hamed Hemati, Marco Schreyer, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Driven by advances in artificial intelligence, deep-le…
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schürholt, Dimche Kostadinov, Damian Borth
Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still…
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
Marco Schreyer, Timur Sattarov, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries. Recently, driven by the a…
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
Linus Scheibenreif, Michael Mommert, Damian Borth
Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problemati…
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…
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…
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…
Adversarial Learning of Deepfakes in Accounting
Marco Schreyer, Timur Sattarov, Bernd Reimer +1
Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregat…
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
Marco Schreyer, Timur Sattarov, Christian Schulze +2
The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derive…
Adversarial Defense based on Structure-to-Signal Autoencoders
Joachim Folz, Sebastian Palacio, Joern Hees +2
Adversarial attack methods have demonstrated the fragility of deep neural networks. Their imperceptible perturbations are frequently able fool classifiers into potentially dangerou…
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Marco Schreyer, Timur Sattarov, Damian Borth +2
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of appl…
Large-Scale Deep Learning on the YFCC100M Dataset
Karl Ni, Roger Pearce, Kofi Boakye +4
We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image an…