Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs (bibtex)
by E. Laude, J.-H. Lange, J. Schüpfer, C. Domokos, L. Leal-Taixé, F. R. Schmidt, B. Andres and D. Cremers
Reference:
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 Entry:
@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@inproceedings{laude-et-al-transductive,
 author = {E. Laude and J.-H. Lange and J. Schüpfer and C. Domokos and L. Leal-Taixé and F. R. Schmidt and B. Andres and D. Cremers},
 title = {Discrete-Continuous {ADMM} for Transductive Inference in Higher-Order {MRF}s},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2018},
 titleurl = {laude-2018-discrete-continuous.pdf},
}
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Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs (bibtex)
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs (bibtex)
by E. Laude, J.-H. Lange, J. Schüpfer, C. Domokos, L. Leal-Taixé, F. R. Schmidt, B. Andres and D. Cremers
Reference:
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 Entry:
@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@inproceedings{laude-et-al-transductive,
 author = {E. Laude and J.-H. Lange and J. Schüpfer and C. Domokos and L. Leal-Taixé and F. R. Schmidt and B. Andres and D. Cremers},
 title = {Discrete-Continuous {ADMM} for Transductive Inference in Higher-Order {MRF}s},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2018},
 titleurl = {laude-2018-discrete-continuous.pdf},
}
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members:laudee

Table of Contents

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


Teaching

Winter Term 2019/20

Summer Term 2018

Winter Term 2017/18

Summer Term 2017

Summer Term 2016