Sören Dittmer | DeepAI

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∙ 07/25/2023

Reinterpreting survival analysis in the universal approximator age

Survival analysis is an integral part of the statistical toolbox. Howeve...

0 Sören Dittmer, et al. ∙

∙ 07/19/2023

Bayesian view on the training of invertible residual networks for solving linear inverse problems

Learning-based methods for inverse problems, adapting to the data's inhe...

0 Clemens Arndt, et al. ∙

∙ 06/02/2023

Invertible residual networks in the context of regularization theory for linear inverse problems

Learned inverse problem solvers exhibit remarkable performance in applic...

0 Clemens Arndt, et al. ∙

∙ 10/21/2022

Navigating the challenges in creating complex data systems: a development philosophy

In this perspective, we argue that despite the democratization of powerf...

0 Sören Dittmer, et al. ∙

∙ 09/12/2022

SELTO: Sample-Efficient Learned Topology Optimization

We present a sample-efficient deep learning strategy for topology optimi...

0 Sören Dittmer, et al. ∙

∙ 06/16/2022

Classification of datasets with imputed missing values: does imputation quality matter?

Classifying samples in incomplete datasets is a common aim for machine l...

29 Tolou Shadbahr, et al. ∙

∙ 06/09/2022

Unsupervised Learning of the Total Variation Flow

The total variation (TV) flow generates a scale-space representation of ...

0 Tamara G. Grossmann, et al. ∙

∙ 08/06/2020

Learned convex regularizers for inverse problems

We consider the variational reconstruction framework for inverse problem...

0 Subhadip Mukherjee, et al. ∙

∙ 07/03/2020

Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset

Magnetic particle imaging (MPI) is an imaging modality exploiting the no...

0 Sören Dittmer, et al. ∙

∙ 07/03/2020

Ground Truth Free Denoising by Optimal Transport

We present a learned unsupervised denoising method for arbitrary types o...

0 Sören Dittmer, et al. ∙

∙ 07/10/2019

A Projectional Ansatz to Reconstruction

Recently the field of inverse problems has seen a growing usage of mathe...

7 Sören Dittmer, et al. ∙

∙ 12/10/2018

Regularization by architecture: A deep prior approach for inverse problems

The present paper studies the so called deep image prior (DIP) technique...

12 Sören Dittmer, et al. ∙

∙ 12/06/2018

Singular Values for ReLU Layers

Despite their prevalence in neural networks we still lack a thorough the...

0 Sören Dittmer, et al. ∙

∙ 06/25/2018

Analysis of Invariance and Robustness via Invertibility of ReLU-Networks

Studying the invertibility of deep neural networks (DNNs) provides a pri...

0 Jens Behrmann, et al. ∙

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