Thomas Möllenhoff

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 37

Top Topics

Convex Relaxation

Markov Random Field

Top Venues

Publications

Etash Kumar Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugène Ndiaye
Conformal Prediction via Regression-as-Classification. ICLR (2024)
Nico Daheim, Thomas Möllenhoff, Edoardo M. Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan
Model Merging by Uncertainty-Based Gradient Matching. ICLR (2024)
Etash Kumar Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugène Ndiaye
Conformal Prediction via Regression-as-Classification. CoRR (2024)
Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
Variational Learning is Effective for Large Deep Networks. CoRR (2024)
Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan
Model Merging by Uncertainty-Based Gradient Matching. CoRR (2023)
Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Lie-Group Bayesian Learning Rule. CoRR (2023)
Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Lie-Group Bayesian Learning Rule. AISTATS (2023)
Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data. CoRR (2023)
Thomas Möllenhoff, Mohammad Emtiyaz Khan
SAM as an Optimal Relaxation of Bayes. ICLR (2023)
Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data. NeurIPS (2023)
Zhenzhang Ye, Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers
A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces. Int. J. Comput. Vis. 131 (1) (2023)
Thomas Möllenhoff, Mohammad Emtiyaz Khan
SAM as an Optimal Relaxation of Bayes. CoRR (2022)
Hannah Dröge, Thomas Möllenhoff, Michael Möller
Non-Smooth Energy Dissipating Networks. ICIP (2022)
Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. SIAM J. Imaging Sci. 15 (3) (2022)
Zhenzhang Ye, Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers
Sublabel-Accurate Multilabeling Meets Product Label Spaces. GCPR (2021)
Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. CoRR (2021)
Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. CoRR (2020)
Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. AISTATS (2020)
Thomas Möllenhoff, Daniel Cremers
Flat Metric Minimization with Applications in Generative Modeling. CoRR (2019)
Pierre Bréchet, Tao Wu, Thomas Möllenhoff, Daniel Cremers
Informative GANs via Structured Regularization of Optimal Transport. CoRR (2019)
Michael Möller, Thomas Möllenhoff, Daniel Cremers
Controlling Neural Networks via Energy Dissipation. CoRR (2019)
Michael Möller, Thomas Möllenhoff, Daniel Cremers
Controlling Neural Networks via Energy Dissipation. ICCV (2019)
Thomas Möllenhoff, Daniel Cremers
Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus. CoRR (2019)
Thomas Möllenhoff, Daniel Cremers
Lifting Vectorial Variational Problems: A Natural Formulation Based on Geometric Measure Theory and Discrete Exterior Calculus. CVPR (2019)
Thomas Möllenhoff, Daniel Cremers
Flat Metric Minimization with Applications in Generative Modeling. ICML (2019)
Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers
Combinatorial Preconditioners for Proximal Algorithms on Graphs. AISTATS (2018)
Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading. CVPR (2018)
Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers
Proximal Backpropagation. ICLR (Poster) (2018)
Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers
Combinatorial Preconditioners for Proximal Algorithms on Graphs. CoRR (2018)
Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers
Proximal Backpropagation. CoRR (2017)
Thomas Möllenhoff, Daniel Cremers
Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems. ICCV (2017)
Thomas Möllenhoff, Daniel Cremers
Precise Relaxation of the Mumford-Shah Functional. CoRR (2016)
Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. CoRR (2016)
Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers
Sublabel-Accurate Relaxation of Nonconvex Energies. CVPR (2016)
Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. ECCV (1) (2016)
Thomas Möllenhoff, Evgeny Strekalovskiy, Michael Möller, Daniel Cremers
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings. SIAM J. Imaging Sci. 8 (2) (2015)
Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers
Sublabel-Accurate Relaxation of Nonconvex Energies. CoRR (2015)
Thomas Möllenhoff, Evgeny Strekalovskiy, Michael Möller, Daniel Cremers
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings. CoRR (2014)
Thomas Möllenhoff, Evgeny Strekalovskiy, Michael Möller, Daniel Cremers
Low Rank Priors for Color Image Regularization. EMMCVPR (2014)
Thomas Möllenhoff, Claudia Nieuwenhuis, Eno Töppe, Daniel Cremers
Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints. EMMCVPR (2013)