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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Research Interests

Convex and Nonconvex Optimization for Machine Learning and Computer Vision, Convex Relaxation Methods

Bio

I'm a PhD student in computer science at the Computer Vision Group TUM headed by Prof. Daniel Cremers. In my research I focus on Numerical Optimization for Machine Learning and Computer Vision and Convex Relaxation Methods.

I received my Bachelor's degree in Computer Science from the University of Würzburg in 2013 and my Master's degree in Informatics (minor Mathematics) in 2015 from the Technical University of Munich.

Publications


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Journal Articles
2020
[]Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity (E. Laude, P. Ochs and D. Cremers), In Journal of Optimization Theory and Applications, volume 184, 2020.  [bibtex] [arXiv:1907.04306]
Preprints
2021
[]Lifting the convex conjugate in Lagrangian relaxations: A Tractable Approach for Continuous Markov Random Fields (H. Bauermeister, E. Laude, T. Moellenhoff, M. Moeller and D. Cremers), In arXiv preprint, 2021.  [bibtex] [arXiv:2107.06028]
Conference and Workshop Papers
2021
[]Bregman Proximal Gradient Algorithms for Deep Matrix Factorization (M. C. Mukkamala, F. Westerkamp, E. Laude, D. Cremers and P. Ochs), In Scale Space and Variational Methods in Computer Vision (A Elmoataz, J Fadili, Y Quéau, J Rabin, L Simon, eds.), Springer International Publishing, 2021.  [bibtex] [arXiv:1910.03638]
2020
[]Distributed Photometric Bundle Adjustment (N Demmel, M Gao, E Laude, T Wu and D Cremers), In International Conference on 3D Vision (3DV), 2020. ([project page][code]) [bibtex] [pdf]Oral Presentation
2019
[]Optimization of Inf-Convolution Regularized Nonconvex Composite Problems (E. Laude, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.  [bibtex] [pdf]
2018
[]A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization (E. Laude, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.  [bibtex] [pdf]
[]Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs (E. Laude, J.-H. Lange, J. Schüpfer, C. Domokos, L. Leal-Taixé, F. R. Schmidt, B. Andres and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.  [bibtex] [pdf]
2016
[]Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies (E. Laude, T. Möllenhoff, M. Moeller, J. Lellmann and D. Cremers), In European Conference on Computer Vision (ECCV), 2016. ([supp] [code]) [bibtex] [pdf]
[]Sublabel-Accurate Relaxation of Nonconvex Energies (T. Möllenhoff, E. Laude, M. Moeller, J. Lellmann and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ([supp] [code]) [bibtex] [pdf]Oral Presentation, Received the Best Paper Honorable Mention Award at CVPR 2016
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Teaching

Winter Term 2019/20

Summer Term 2018

Winter Term 2017/18

Summer Term 2017

Summer Term 2016

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023.

31.08.2022

Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.

17.07.2022

MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.

17.07.2022

AI Symposium

On July 22nd 2022, we are organizing a Symposium on AI within the Technology Forum of the Bavarian Academy of Sciences.

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