352 citations · 411 across the 9 of their papers we have counts for
13 papers
Explainable AI improves task performance in human-AI collaboration
Julian Senoner, Simon Schallmoser, Bernhard Kratzwald +2
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate i…
Addressing distributional shifts in operations management: The case of order fulfillment in customized production
Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic +2
To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lin…
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald +2
Question answering (QA) has recently shown impressive results for answering questions from customized domains. Yet, a common challenge is to adapt QA models to an unseen target dom…
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel
Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated…
Learning a Cost-Effective Annotation Policy for Question Answering
Bernhard Kratzwald, Stefan Feuerriegel, Huan Sun
State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems…
Practical Annotation Strategies for Question Answering Datasets
Bernhard Kratzwald, Xiang Yue, Huan Sun +1
Annotating datasets for question answering (QA) tasks is very costly, as it requires intensive manual labor and often domain-specific knowledge. Yet strategies for annotating QA da…
RankQA: Neural Question Answering with Answer Re-Ranking
Bernhard Kratzwald, Anna Eigenmann, Stefan Feuerriegel
The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently,…
Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems
Nil-Jana Akpinar, Bernhard Kratzwald, Stefan Feuerriegel
Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurren…
Adaptive Document Retrieval for Deep Question Answering
Bernhard Kratzwald, Stefan Feuerriegel
State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural netwo…
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Bernhard Kratzwald, Stefan Feuerriegel
Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate t…
Deep learning for affective computing: text-based emotion recognition in decision support
Bernhard Kratzwald, Suzana Ilic, Mathias Kraus +2
Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individu…
Face Translation between Images and Videos using Identity-aware CycleGAN
Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel +2
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exi…
Improving Video Generation for Multi-functional Applications
Bernhard Kratzwald, Zhiwu Huang, Danda Pani Paudel +2
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications. Our improved video GAN model d…