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03/08/2023
The Lie-Group Bayesian Learning Rule
The Bayesian Learning Rule provides a framework for generic algorithm de...
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10/04/2022
SAM as an Optimal Relaxation of Bayes
Sharpness-aware minimization (SAM) and related adversarial deep-learning...
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07/13/2021
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields
Dual decomposition approaches in nonconvex optimization may suffer from ...
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02/27/2020
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning
Structured convex optimization on weighted graphs finds numerous applica...
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12/04/2019
Informative GANs via Structured Regularization of Optimal Transport
We tackle the challenge of disentangled representation learning in gener...
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05/12/2019
Flat Metric Minimization with Applications in Generative Modeling
We take the novel perspective to view data not as a probability distribu...
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05/02/2019
Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus
Numerous tasks in imaging and vision can be formulated as variational pr...
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04/05/2019
Controlling Neural Networks via Energy Dissipation
The last decade has shown a tremendous success in solving various comput...
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01/16/2018
Combinatorial Preconditioners for Proximal Algorithms on Graphs
We present a novel preconditioning technique for proximal optimization m...
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11/21/2016
Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems
In this work we show how sublabel-accurate multilabeling approaches can ...
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04/07/2016
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies
Convex relaxations of nonconvex multilabel problems have been demonstrat...
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12/04/2015
Sublabel-Accurate Relaxation of Nonconvex Energies
We propose a novel spatially continuous framework for convex relaxations...
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07/07/2014