2 citations · 2 across the 2 of their papers we have counts for
2 papers
stat.ME2026
A statistical perspective on transformers for small longitudinal cohort data
Kiana Farhadyar, Maren Hackenberg, Kira Ahrens +10
Modeling of longitudinal cohort data typically involves complex temporal dependencies between multiple variables. There, the transformer architecture, which has been highly success…
stat.ML2022★ 2 cited
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods
Göran Köber, Raffael Kalisch, Lara Puhlmann +3
When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by art…