166 citations · 424 across the 44 of their papers we have counts for
4 papers · 1 filter
Statistical guarantees for sparse deep learning
Johannes Lederer
Neural networks are becoming increasingly popular in applications, but our mathematical understanding of their potential and limitations is still limited. In this paper, we further…
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as no…
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering
Rebecca Marion, Johannes Lederer, Bernadette Govaerts +1
Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g. there are highly correlated groups of variables…
Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method
Yannick Düren, Johannes Lederer, Li-Xuan Qin
Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for "normalizing" seq…