Zobrazeno 1 - 10
of 189
pro vyhledávání: '"assignment flows"'
We introduce a novel generative model for the representation of joint probability distributions of a possibly large number of discrete random variables. The approach uses measure transport by randomized assignment flows on the statistical submanifold
Externí odkaz:
http://arxiv.org/abs/2406.04527
Akademický článek
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Autor:
Schwarz, Jonathan, Cassel, Jonas, Boll, Bastian, Gärttner, Martin, Albers, Peter, Schnörr, Christoph
This paper introduces assignment flows for density matrices as state spaces for representing and analyzing data associated with vertices of an underlying weighted graph. Determining an assignment flow by geometric integration of the defining dynamica
Externí odkaz:
http://arxiv.org/abs/2307.00075
Metric data labeling refers to the task of assigning one of multiple predefined labels to every given datapoint based on the metric distance between label and data. This assignment of labels typically takes place in a spatial or spatio-temporal conte
Externí odkaz:
http://arxiv.org/abs/2111.02543
We introduce a novel algorithm for estimating optimal parameters of linearized assignment flows for image labeling. An exact formula is derived for the parameter gradient of any loss function that is constrained by the linear system of ODEs determini
Externí odkaz:
http://arxiv.org/abs/2108.02571
Autor:
Jonathan Schwarz, Jonas Cassel, Bastian Boll, Martin Gärttner, Peter Albers, Christoph Schnörr
Publikováno v:
Entropy, Vol 25, Iss 9, p 1253 (2023)
This paper introduces assignment flows for density matrices as state spaces for representation and analysis of data associated with vertices of an underlying weighted graph. Determining an assignment flow by geometric integration of the defining dyna
Externí odkaz:
https://doaj.org/article/801669b5c8524dec9236fe26e3762cf4
This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given. The resulting self-assignment flow takes a pairwise data affinity matrix as input data and maximize
Externí odkaz:
http://arxiv.org/abs/1911.03472
The assignment flow recently introduced in the J. Math. Imaging and Vision 58/2 (2017), constitutes a high-dimensional dynamical system that evolves on an elementary statistical manifold and performs contextual labeling (classification) of data given
Externí odkaz:
http://arxiv.org/abs/2002.11571
Autor:
Savarino, Fabrizio, Schnörr, Christoph
Assignment flows denote a class of dynamical models for contextual data labeling (classification) on graphs. We derive a novel parametrization of assignment flows that reveals how the underlying information geometry induces two processes for assignme
Externí odkaz:
http://arxiv.org/abs/1910.07287
Akademický článek
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