28 citations · 41 across the 4 of their papers we have counts for
5 papers · 1 filter
Generalized Multimodal ELBO
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supe…
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Ričards Marcinkevičs, Julia E. Vogt
Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in seq…
Generation of Differentially Private Heterogeneous Electronic Health Records
Kieran Chin-Cheong, Thomas Sutter, Julia E. Vogt
Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advant…
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier +5
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into…
Probabilistic Clustering of Time-Evolving Distance Data
Julia E. Vogt, Marius Kloft, Stefan Stark +4
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the in…