1 citations · 1 across the 4 of their papers we have counts for
5 papers
Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction
Paulo Yanez Sarmiento, Pia Francesca Rissom, Manuel Pfeuffer +6
Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical representation of protein sequences w…
Bayesian meta-learning for modeling Alzheimer's disease progression
Clara Hoffmann, Nadja Klein
Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment. Typically, practitioners seek…
QDSB: Quantized Diffusion Schrödinger Bridges
Tobias Fuchs, Florian Kalinke, Nadja Klein
Learning generative models in settings where the source and target distributions are only specified through unpaired samples is gaining in importance. Here, one frequently-used mod…
BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
Marta S. Lemanczyk, Lucas Kock, Johanna Schlimme +2
Biological data sets are often high-dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable fea…
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Paulo Yanez Sarmiento, Simon Witzke, Nadja Klein +1
Explainability is a key component in many applications involving deep neural networks (DNNs). However, current explanation methods for DNNs commonly leave it to the human observer…