Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Hacker, Celia"'
Geometric deep learning extends deep learning to incorporate information about the geometry and topology data, especially in complex domains like graphs. Despite the popularity of message passing in this field, it has limitations such as the need for
Externí odkaz:
http://arxiv.org/abs/2312.08515
In multi-parameter persistence, the matching distance is defined as the supremum of weighted bottleneck distances on the barcodes given by the restriction of persistence modules to lines with a positive slope. In the case of finitely presented bi-per
Externí odkaz:
http://arxiv.org/abs/2312.02955
Autor:
Bapat, Asilata, Brooks, Robyn, Hacker, Celia, Landi, Claudia, Mahler, Barbara I., Stephenson, Elizabeth R.
The exact computation of the matching distance for multi-parameter persistence modules is an active area of research in computational topology. Achieving an easily obtainable exact computation of this distance would allow multi-parameter persistent h
Externí odkaz:
http://arxiv.org/abs/2210.12868
Autor:
Hacker, Celia, Rieck, Bastian
Graph embedding techniques are a staple of modern graph learning research. When using embeddings for downstream tasks such as classification, information about their stability and robustness, i.e., their susceptibility to sources of noise, stochastic
Externí odkaz:
http://arxiv.org/abs/2206.08252
Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks. The distribution of these random walks evolves according t
Externí odkaz:
http://arxiv.org/abs/2205.14092
At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Lap
Externí odkaz:
http://arxiv.org/abs/2203.08571
Although there is no doubt that multi-parameter persistent homology is a useful tool to analyse multi-variate data, efficient ways to compute these modules are still lacking in the available topological data analysis toolboxes. Other issues such as i
Externí odkaz:
http://arxiv.org/abs/2011.14967
Autor:
Hacker, Celia
We present a novel method of associating Euclidean features to simplicial complexes, providing a way to use them as input to statistical and machine learning tools. This method extends the node2vec algorithm to simplices of higher dimensions, providi
Externí odkaz:
http://arxiv.org/abs/2010.05636
Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks. The distribution of these random walks evolves according t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea1ec3aee16cf5774ec94d7804bf73a2
https://ora.ox.ac.uk/objects/uuid:ace13532-0f5b-4f74-ac28-7a5cc17363ab
https://ora.ox.ac.uk/objects/uuid:ace13532-0f5b-4f74-ac28-7a5cc17363ab
Autor:
Hacker, Celia Camille
The field of computational topology has developed many powerful tools to describe the shape of data, offering an alternative point of view from classical statistics. This results in a variety of complex structures that are not always directly amenabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::339b105b008a1a82da99e0c35fa561c4
https://infoscience.epfl.ch/record/295976
https://infoscience.epfl.ch/record/295976