68 citations · 213 across the 20 of their papers we have counts for
5 papers · 1 filter
On the Limitations of Multimodal VAEs
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong +2
Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhi…
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…
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Laura Manduchi, Kieran Chin-Cheong, Holger Michel +2
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Fo…
A Deep Variational Approach to Clustering Survival Data
Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi +9
In this work, we study the problem of clustering survival data a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to clus…
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…