Zobrazeno 1 - 10
of 12 945
pro vyhledávání: '"RIECK, A."'
Autor:
Akitaya, Hugo A., Fekete, Sándor P., Kramer, Peter, Molaei, Saba, Rieck, Christian, Stock, Frederick, Wallner, Tobias
We consider algorithmic problems motivated by modular robotic reconfiguration, for which we are given $n$ square-shaped modules (or robots) in a (labeled or unlabeled) start configuration and need to find a schedule of sliding moves to transform it i
Externí odkaz:
http://arxiv.org/abs/2412.05523
Autor:
Behrooznia, Nastaran, Brenner, Sofia, Merino, Arturo, Mütze, Torsten, Rieck, Christian, Verciani, Francesco
We construct facet-Hamiltonian cycles in the $B$-permutahedron, resolving a conjecture raised in a recent paper by Akitaya, Cardinal, Felsner, Kleist and Lauff [arxiv.org/abs/2411.02172].
Externí odkaz:
http://arxiv.org/abs/2412.02584
The Laser Interferometer Space Antenna (LISA) will feature a prominent anisotropic astrophysical stochastic gravitational wave signal, arising from the tens of millions of unresolved mHz white dwarf binaries in the Milky Way: the Galactic foreground.
Externí odkaz:
http://arxiv.org/abs/2410.23260
Autor:
Rieck, Bastian
This overview article makes the case for how topological concepts can enrich research in machine learning. Using the Euler Characteristic Transform (ECT), a geometrical-topological invariant, as a running example, I present different use cases that r
Externí odkaz:
http://arxiv.org/abs/2410.17760
The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed by transforming spatial features from the graph domain into spectral
Externí odkaz:
http://arxiv.org/abs/2410.10546
Autor:
Röell, Ernst, Rieck, Bastian
The Euler Characteristic Transform (ECT) is a powerful invariant for assessing geometrical and topological characteristics of a large variety of objects, including graphs and embedded simplicial complexes. Although the ECT is invertible in theory, no
Externí odkaz:
http://arxiv.org/abs/2410.18987
Deep neural networks often learn similar internal representations, both across different models and within their own layers. While inter-network similarities have enabled techniques such as model stitching and merging, intra-network similarities pres
Externí odkaz:
http://arxiv.org/abs/2410.04941
The Euler Characteristic Transform (ECT) is an efficiently-computable geometrical-topological invariant that characterizes the global shape of data. In this paper, we introduce the Local Euler Characteristic Transform ($\ell$-ECT), a novel extension
Externí odkaz:
http://arxiv.org/abs/2410.02622
Autor:
Ballester, Rubén, Röell, Ernst, Schmid, Daniel Bin, Alain, Mathieu, Escalera, Sergio, Casacuberta, Carles, Rieck, Bastian
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural netw
Externí odkaz:
http://arxiv.org/abs/2410.02392
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neural network models still struggle to capture higher-order information,
Externí odkaz:
http://arxiv.org/abs/2409.08217