Emanuel LaudePhD StudentTechnische Universität München
Department of Computer Science
Convex and Nonconvex Optimization for Machine Learning and Computer Vision, Convex Relaxation Methods
I'm a second-year PhD student in computer science at the Computer Vision Group TUM headed by Prof. Daniel Cremers. In my research I focus on Convex and Nonconvex Optimization for Machine Learning and Computer Vision and Convex Relaxation Methods.
I received my Master's degree (with high distinction) in Informatics (minor Mathematics) in 2015 from the Technical University of Munich and my Bachelor's degree (with distinction) in Computer Science in 2013 from the University of Würzburg.
|Conference and Workshop Papers|
|Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs , In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [bib] [pdf]|
|A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization , In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018. [bib] [pdf]|
|Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies , In European Conference on Computer Vision (ECCV), 2016.([supp] [code]) [bib] [pdf]|
|Sublabel-Accurate Relaxation of Nonconvex Energies , In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.([supp] [code]) [bib] [pdf]Oral Presentation, Received the Best Paper Honorable Mention Award at CVPR 2016|