b'Damian Borth'

research

∙ 09/04/2023

FinDiff: Diffusion Models for Financial Tabular Data Generation

The sharing of microdata, such as fund holdings and derivative instrumen...

0 Timur Sattarov, et al. ∙

research

∙ 07/04/2023

Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

Deep learning methods have proven to be a powerful tool in the analysis ...

0 Michael Mommert, et al. ∙

research

∙ 06/19/2023

Partial Hypernetworks for Continual Learning

Hypernetworks mitigate forgetting in continual learning (CL) by generati...

0 Hamed Hemati, et al. ∙

research

∙ 06/09/2023

Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech

Effective speech emotional representations play a key role in Speech Emo...

7 Shijun Wang, et al. ∙

research

∙ 04/26/2023

Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models

With growing size of Neural Networks (NNs), model sparsification to redu...

6 Dominik Honegger, et al. ∙

research

∙ 03/02/2023

Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities

State-of-the-art Text-To-Speech (TTS) models are capable of producing hi...

6 Shijun Wang, et al. ∙

research

∙ 01/26/2023

Class-Incremental Learning with Repetition

Real-world data streams naturally include the repetition of previous con...

13 Hamed Hemati, et al. ∙

research

∙ 10/26/2022

Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing

The International Standards on Auditing require auditors to collect reas...

12 Marco Schreyer, et al. ∙

research

∙ 09/29/2022

Model Zoos: A Dataset of Diverse Populations of Neural Network Models

In the last years, neural networks (NN) have evolved from laboratory env...

33 Konstantin Schürholt, et al. ∙

research

∙ 09/29/2022

Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights

Learning representations of neural network weights given a model zoo is ...

12 Konstantin Schürholt, et al. ∙

research

∙ 09/19/2022

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

Detecting accounting anomalies is a recurrent challenge in financial sta...

10 Ricardo Müller, et al. ∙

research

∙ 08/26/2022

Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

The ongoing 'digital transformation' fundamentally changes audit evidenc...

12 Marco Schreyer, et al. ∙

research

∙ 08/09/2022

Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition

Speech Emotion Recognition (SER) is crucial for human-computer interacti...

0 Shijun Wang, et al. ∙

research

∙ 07/22/2022

Hyper-Representations for Pre-Training and Transfer Learning

Learning representations of neural network weights given a model zoo is ...

7 Konstantin Schürholt, et al. ∙

research

∙ 12/25/2021

Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data

International audit standards require the direct assessment of a financi...

4 Hamed Hemati, et al. ∙

research

∙ 12/07/2021

Saliency Diversified Deep Ensemble for Robustness to Adversaries

Deep learning models have shown incredible performance on numerous image...

18 Alex Bogun, et al. ∙

research

∙ 10/28/2021

Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction

Self-Supervised Learning (SSL) has been shown to learn useful and inform...

6 Konstantin Schürholt, et al. ∙

research

∙ 10/27/2021

Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning

Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, i...

5 Shijun Wang, et al. ∙

research

∙ 09/23/2021

Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks

International audit standards require the direct assessment of a financi...

7 Marco Schreyer, et al. ∙

research

∙ 09/21/2021

Heterogeneous Ensemble for ESG Ratings Prediction

Over the past years, topics ranging from climate change to human rights ...

5 Tim Krappel, et al. ∙

research

∙ 09/21/2021

Learning Interpretable Concept Groups in CNNs

We propose a novel training methodology – Concept Group Learning (CGL) –...

10 Saurabh Varshneya, et al. ∙

research

∙ 08/31/2021

Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

Air pollution is a major driver of climate change. Anthropogenic emissio...

16 Linus Scheibenreif, et al. ∙

research

∙ 07/22/2021

Power Plant Classification from Remote Imaging with Deep Learning

Satellite remote imaging enables the detailed study of land use patterns...

21 Michael Mommert, et al. ∙

research

∙ 04/13/2021

NoiseVC: Towards High Quality Zero-Shot Voice Conversion

Voice conversion (VC) is a task that transforms voice from target audio ...

9 Shijun Wang, et al. ∙

research

∙ 03/26/2021

Continual Speaker Adaptation for Text-to-Speech Synthesis

Training a multi-speaker Text-to-Speech (TTS) model from scratch is comp...

19 Hamed Hemati, et al. ∙

research

∙ 12/13/2020

Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks

Nowadays, organizations collect vast quantities of sensitive information...

9 Marco Schreyer, et al. ∙

research

∙ 11/23/2020

Characterization of Industrial Smoke Plumes from Remote Sensing Data

The major driver of global warming has been identified as the anthropoge...

8 Michael Mommert, et al. ∙

research

∙ 11/12/2020

Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement

Recent neural Text-to-Speech (TTS) models have been shown to perform ver...

0 Hamed Hemati, et al. ∙

research

∙ 08/06/2020

Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks

The audit of financial statements is designed to collect reasonable assu...

11 Marco Schreyer, et al. ∙

research

∙ 07/15/2020

Facial Recognition: A cross-national Survey on Public Acceptance, Privacy, and Discrimination

With rapid advances in machine learning (ML), more of this technology is...

0 Léa Steinacker, et al. ∙

research

∙ 06/18/2020

An Investigation of the Weight Space for Version Control of Neural Networks

Deployed Deep Neural Networks (DNNs) are often trained further to improv...

0 Konstantin Schürholt, et al. ∙

research

∙ 05/04/2020

Neural Networks and Value at Risk

Utilizing a generative regime switching framework, we perform Monte-Carl...

0 Alexander Arimond, et al. ∙

research

∙ 10/09/2019

Adversarial Learning of Deepfakes in Accounting

Nowadays, organizations collect vast quantities of accounting relevant t...

38 Marco Schreyer, et al. ∙

research

∙ 08/02/2019

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks

The detection of fraud in accounting data is a long-standing challenge i...

15 Marco Schreyer, et al. ∙

research

∙ 08/09/2018

Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation

The integration of information acquired with different modalities, spati...

0 Benjamin Bischke, et al. ∙

research

∙ 03/22/2018

What do Deep Networks Like to See?

We propose a novel way to measure and understand convolutional neural ne...

0 Sebastian Palacio, et al. ∙

research

∙ 03/21/2018

Adversarial Defense based on Structure-to-Signal Autoencoders

Adversarial attack methods have demonstrated the fragility of deep neura...

0 Joachim Folz, et al. ∙

research

∙ 12/28/2017

Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)

Social multimedia users are increasingly sharing all kinds of data about...

0 Mario Michael Krell, et al. ∙

research

∙ 09/18/2017

Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks

The increased availability of high resolution satellite imagery allows t...

0 Benjamin Bischke, et al. ∙

research

∙ 09/15/2017

Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

Learning to detect fraud in large-scale accounting data is one of the lo...

0 Marco Schreyer, et al. ∙

research

∙ 07/25/2016

An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia

Efficient usage of the knowledge provided by the Linked Data community i...

0 Jörn Hees, et al. ∙

research

∙ 07/13/2016

AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis

Recently, sound recognition has been used to identify sounds, such as ca...

0 Sebastian Sager, et al. ∙

research

∙ 11/21/2015

Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets

We consider the visual sentiment task of mapping an image to an adjectiv...

0 Takuya Narihira, et al. ∙

research

∙ 03/13/2015

The YLI-MED Corpus: Characteristics, Procedures, and Plans

The YLI Multimedia Event Detection corpus is a public-domain index of vi...

0 Julia Bernd, et al. ∙

research

∙ 02/11/2015

Large-Scale Deep Learning on the YFCC100M Dataset

We present a work-in-progress snapshot of learning with a 15 billion par...

0 Karl Ni, et al. ∙

research

∙ 10/30/2014

DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks

This paper introduces a visual sentiment concept classification method b...

0 Tao Chen, et al. ∙

Damian Borth

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