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Emanuel Laude

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Adaptive proximal gradient methods are universal without approximation

Feb 09, 2024
Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos

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Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields

Jul 13, 2021
Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Daniel Cremers

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Bregman Proximal Framework for Deep Linear Neural Networks

Oct 08, 2019
Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

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Optimization of Inf-Convolution Regularized Nonconvex Composite Problems

Mar 27, 2019
Emanuel Laude, Tao Wu, Daniel Cremers

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Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs

Apr 28, 2018
Emanuel Laude, Jan-Hendrik Lange, Jonas Schüpfer, Csaba Domokos, Laura Leal-Taixé, Frank R. Schmidt, Bjoern Andres, Daniel Cremers

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Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

Oct 10, 2016
Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Jan Lellmann, Daniel Cremers

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Sublabel-Accurate Relaxation of Nonconvex Energies

Dec 04, 2015
Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers

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