1 citations · 1 across the 3 of their papers we have counts for
4 papers
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
Kacper Kapuśniak, Manuel Burger, Gunnar Rätsch +1
The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations. Existing techniques often neglect intricate structural details, empha…
A Generative Model of Words and Relationships from Multiple Sources
Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
Neural language models are a powerful tool to embed words into semantic vector spaces. However, learning such models generally relies on the availability of abundant and diverse tr…
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images
Christian Widmer, Philipp Drewe, Xinghua Lou +3
Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cell nuclear segmentation in highly anisotropic and noisy…
The Feature Importance Ranking Measure
Alexander Zien, Nicole Kraemer, Soeren Sonnenburg +1
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access…