131 citations · 315 across the 16 of their papers we have counts for
11 papers · 1 filter
FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems
Fan Zhang, Daniel Kreuter, Carlos Esteve-Yagüe +11
Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistica…
The curious case of the test set AUROC
Michael Roberts, Alon Hazan, Sören Dittmer +2
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace. In partic…
Recent Methodological Advances in Federated Learning for Healthcare
Fan Zhang, Daniel Kreuter, Yichen Chen +10
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the util…
Reinterpreting survival analysis in the universal approximator age
Sören Dittmer, Michael Roberts, Jacobus Preller +4
Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently g…
SELTO: Sample-Efficient Learned Topology Optimization
Sören Dittmer, David Erzmann, Henrik Harms +1
Recent developments in Deep Learning (DL) suggest a vast potential for Topology Optimization (TO). However, while there are some promising attempts, the subfield still lacks a firm…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov +3
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.…
A Projectional Ansatz to Reconstruction
Sören Dittmer, Peter Maass
Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors. Based on first principles, we develop a…
Regularization by architecture: A deep prior approach for inverse problems
Sören Dittmer, Tobias Kluth, Peter Maass +1
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in i…
Singular Values for ReLU Layers
Sören Dittmer, Emily J. King, Peter Maass
Despite their prevalence in neural networks we still lack a thorough theoretical characterization of ReLU layers. This paper aims to further our understanding of ReLU layers by stu…
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Jens Behrmann, Sören Dittmer, Pascal Fernsel +1
Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models. Despite being a promising diagn…