47 citations · 76 across the 13 of their papers we have counts for
21 papers
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…
Copula-Based Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressi…
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks
Leni Ven, Johannes Lederer
Deep learning requires several design choices, such as the nodes' activation functions and the widths, types, and arrangements of the layers. One consideration when making these ch…
Targeted Deep Learning: Framework, Methods, and Applications
Shih-Ting Huang, Johannes Lederer
Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic sit…
DeepMoM: Robust Deep Learning With Median-of-Means
Shih-Ting Huang, Johannes Lederer
Data used in deep learning is notoriously problematic. For example, data are usually combined from diverse sources, rarely cleaned and vetted thoroughly, and sometimes corrupted on…