Sarah Haffner
Biographic Data
| ID | 216536 |
|---|---|
| NAME | Sarah Haffner |
| GIVEN NAMES | Sarah |
| FAMILY NAME | Haffner |
| SIGNATURE | HAFFNER S |
| AFFILIATIONS | Universität der Bundeswehr München |
| ORCID | 0000-0001-6914-9709 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1981 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
The 2023/24 Views Prediction challenge
Governmental and nongovernmental organizations have increasingly relied on early-warning systems of conflict to support their decisionmaking. Predictions of war intensity as probability distributions prove closer to what policymakers need than point estimates, as they encompass useful representations of both the most likely outcome and the lower-probability risk that conflicts escalate catastrophically. Point-estimate predictions, by contrast, fa…
Taking time seriously
Previous conflict forecasting efforts identified three areas for improvement: the importance of spatiotemporal dependencies and nonlinearities and the further exploitation of latent information in conflict variables, a lack of interpretability in return for high accuracy of complex algorithms, and the need to quantify prediction uncertainty. We predict conflict fatalities with temporal fusion transformers which have several desirable features for…
Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation
Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability …
Frauenhaeuser Gewalt in der Ehe und was Frauen dagegen tun (Women's Refuge)
Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation
Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability …
Frauenhaeuser Gewalt in der Ehe und was Frauen dagegen tun (Women's Refuge)
Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation
Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability …
Taking time seriously
Previous conflict forecasting efforts identified three areas for improvement: the importance of spatiotemporal dependencies and nonlinearities and the further exploitation of latent information in conflict variables, a lack of interpretability in return for high accuracy of complex algorithms, and the need to quantify prediction uncertainty. We predict conflict fatalities with temporal fusion transformers which have several desirable features for…
The 2023/24 Views Prediction challenge
Governmental and nongovernmental organizations have increasingly relied on early-warning systems of conflict to support their decisionmaking. Predictions of war intensity as probability distributions prove closer to what policymakers need than point estimates, as they encompass useful representations of both the most likely outcome and the lower-probability risk that conflicts escalate catastrophically. Point-estimate predictions, by contrast, fa…
Computer Science (3 works) · Artificial Intelligence (2 works) · Engineering (2 works) · Human Factors and Ergonomics (2 works) · Medical emergency (2 works) · Medicine (2 works) · Poison control (2 works) · Psychology (2 works) · Anomaly Detection Techniques and Applications (1 works) · Armed conflict (1 works)