268 citations · 281 across the 7 of their papers we have counts for
16 papers
A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In…
Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons
Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa +4
The Jaccard similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information retrieval, and many other area…
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Laura Manduchi, Matthias Hüser, Julia Vogt +2
Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation le…
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…
Disentangling Factors of Variation Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer +3
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentangleme…
Machine learning for early prediction of circulatory failure in the intensive care unit
Stephanie L. Hyland, Martin Faltys, Matthias Hüser +12
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to proce…