S
Stefan Feuerriegel
Researcher at ETH Zurich
Publications - 226
Citations - 3640
Stefan Feuerriegel is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 25, co-authored 175 publications receiving 2192 citations. Previous affiliations of Stefan Feuerriegel include University of New South Wales & National Institute of Informatics.
Papers
More filters
Journal ArticleDOI
Decision support from financial disclosures with deep neural networks and transfer learning
Mathias Kraus,Stefan Feuerriegel +1 more
TL;DR: The use of deep neural networks for financial decision support is studied and a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures is revealed.
Journal ArticleDOI
Deep learning for affective computing: Text-based emotion recognition in decision support
TL;DR: This work proposes sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition.
Journal ArticleDOI
Emergency response in natural disaster management: Allocation and scheduling of rescue units
TL;DR: A corresponding decision support model is developed that minimizes the sum of completion times of incidents weighted by their severity and can be reduced by up to 81.8%.
Journal ArticleDOI
Deep learning in business analytics and operations research: Models, applications and managerial implications
TL;DR: The objectives of this overview article are to motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits, and investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings.
Posted Content
Deep learning in business analytics and operations research: Models, applications and managerial implications.
TL;DR: In this paper, the authors provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning and highlight the value of customized architectures by proposing a novel deep-embedded network.