Zobrazeno 1 - 10
of 1 006
pro vyhledávání: '"P. Schnörr"'
We consider a Gibbs distribution over all spanning trees of an undirected, edge weighted finite graph, where, up to normalization, the probability of each tree is given by the product of its edge weights. Defining the weighted degree of a node as the
Externí odkaz:
http://arxiv.org/abs/2409.13472
This paper introduces the sigma flow model for the prediction of structured labelings of data observed on Riemannian manifolds, including Euclidean image domains as special case. The approach combines the Laplace-Beltrami framework for image denoisin
Externí odkaz:
http://arxiv.org/abs/2408.15946
Density-based distances (DBDs) offer an elegant solution to the problem of metric learning. By defining a Riemannian metric which increases with decreasing probability density, shortest paths naturally follow the data manifold and points are clustere
Externí odkaz:
http://arxiv.org/abs/2407.09297
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
Spanning trees are an important primitive in many data analysis tasks, when a data set needs to be summarized in terms of its "skeleton", or when a tree-shaped graph over all observations is required for downstream processing. Popular definitions of
Externí odkaz:
http://arxiv.org/abs/2404.06447
This paper introduces a novel generative model for discrete distributions based on continuous normalizing flows on the submanifold of factorizing discrete measures. Integration of the flow gradually assigns categories and avoids issues of discretizin
Externí odkaz:
http://arxiv.org/abs/2402.07846
We present a novel theoretical framework for understanding the expressive power of normalizing flows. Despite their prevalence in scientific applications, a comprehensive understanding of flows remains elusive due to their restricted architectures. E
Externí odkaz:
http://arxiv.org/abs/2402.06578
This paper studies a meta-simplex concept and geometric embedding framework for multi-population replicator dynamics. Central results are two embedding theorems which constitute a formal reduction of multi-population replicator dynamics to single-pop
Externí odkaz:
http://arxiv.org/abs/2401.05918
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
Autor:
Draxler, Felix, Kühmichel, Lars, Rousselot, Armand, Müller, Jens, Schnörr, Christoph, Köthe, Ullrich
Gaussianization is a simple generative model that can be trained without backpropagation. It has shown compelling performance on low dimensional data. As the dimension increases, however, it has been observed that the convergence speed slows down. We
Externí odkaz:
http://arxiv.org/abs/2306.13520