1.6k citations · 2.2k across the 44 of their papers we have counts for
48 papers
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…
Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
Michael Mommert, Nicolas Kesseli, Joëlle Hanna +3
Deep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in…
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…
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech
Shijun Wang, Jón Guðnason, Damian Borth
Effective speech emotional representations play a key role in Speech Emotion Recognition (SER) and Emotional Text-To-Speech (TTS) tasks. However, emotional speech samples are more…
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…
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities
Shijun Wang, Jón Guðnason, Damian Borth
State-of-the-art Text-To-Speech (TTS) models are capable of producing high-quality speech. The generated speech, however, is usually neutral in emotional expression, whereas very o…
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…
Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition
Shijun Wang, Hamed Hemati, Jón Guðnason +1
Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many…
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…
Saliency Diversified Deep Ensemble for Robustness to Adversaries
Alex Bogun, Dimche Kostadinov, Damian Borth
Deep learning models have shown incredible performance on numerous image recognition, classification, and reconstruction tasks. Although very appealing and valuable due to their pr…
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…
Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning
Shijun Wang, Dimche Kostadinov, Damian Borth
Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an attractive research topic as it enables a range of applications like voice customizing, animation produ…
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…
Heterogeneous Ensemble for ESG Ratings Prediction
Tim Krappel, Alex Bogun, Damian Borth
Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners)…
Learning Interpretable Concept Groups in CNNs
Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen +4
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept…
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…
Power Plant Classification from Remote Imaging with Deep Learning
Michael Mommert, Linus Scheibenreif, Joëlle Hanna +1
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land us…
NoiseVC: Towards High Quality Zero-Shot Voice Conversion
Shijun Wang, Damian Borth
Voice conversion (VC) is a task that transforms voice from target audio to source without losing linguistic contents, it is challenging especially when source and target speakers a…