Leemann T, Sackmann M, Thielecke J, Hofmann U (2021)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2021
Publisher: ESANN
Pages Range: 523-528
Conference Proceedings Title: Proceedings of the 29th European Symposium on Artificial Neural Networks
ISBN: 978287587082-7
URI: https://www.esann.org/sites/default/files/proceedings/2021/ES2021-16.pdf
DOI: 10.14428/esann/2021.es2021-16
Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast’s uncertainty. The Multiple Hypotheses Prediction (MHP) approach addresses this problem by providing several hypotheses that represent possible outcomes. Unfortunately, with the common l2 loss function, these hypotheses do not preserve the data distribution’s characteristics. We propose an alternative loss for distribution preserving MHP and review relevant theorems supporting our claims. Furthermore, we empirically show that our approach yields more representative hypotheses on a synthetic and a real-world motion prediction data set. The outputs of the proposed method can directly be used in sampling-based Monte-Carlo methods.
APA:
Leemann, T., Sackmann, M., Thielecke, J., & Hofmann, U. (2021). Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling. In Proceedings of the 29th European Symposium on Artificial Neural Networks (pp. 523-528). online, BE: ESANN.
MLA:
Leemann, Tobias, et al. "Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling." Proceedings of the European Symposium on Artificial Neural Networks (ESANN), online ESANN, 2021. 523-528.
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