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pro vyhledávání: '"Bart, M"'
The NP-hard Odd Cycle Transversal problem asks for a minimum vertex set whose removal from an undirected input graph $G$ breaks all odd cycles, and thereby yields a bipartite graph. The problem is well-known to be fixed-parameter tractable when param
Externí odkaz:
http://arxiv.org/abs/2409.00245
In the Steiner Tree problem we are given an undirected edge-weighted graph as input, along with a set $K$ of vertices called terminals. The task is to output a minimum-weight connected subgraph that spans all the terminals. The famous Dreyfus-Wagner
Externí odkaz:
http://arxiv.org/abs/2406.19819
We introduce a class of trainable nonlinear operators based on semirings that are suitable for use in neural networks. These operators generalize the traditional alternation of linear operators with activation functions in neural networks. Semirings
Externí odkaz:
http://arxiv.org/abs/2405.18805
For a fixed graph $H$, the $H$-SUBGRAPH HITTING problem consists in deleting the minimum number of vertices from an input graph to obtain a graph without any occurrence of $H$ as a subgraph. This problem can be seen as a generalization of VERTEX COVE
Externí odkaz:
http://arxiv.org/abs/2404.16695
For an optimization problem $\Pi$ on graphs whose solutions are vertex sets, a vertex $v$ is called $c$-essential for $\Pi$ if all solutions of size at most $c \cdot OPT$ contain $v$. Recent work showed that polynomial-time algorithms to detect $c$-e
Externí odkaz:
http://arxiv.org/abs/2404.09769
Autor:
van Marrewijk, Bart M., Dandjinou, Charbel, Rustia, Dan Jeric Arcega, Gonzalez, Nicolas Franco, Diallo, Boubacar, Dias, Jérôme, Melki, Paul, Blok, Pieter M.
Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which every pixel must be annotated. A potential strategy to mitigate annotat
Externí odkaz:
http://arxiv.org/abs/2404.02580
PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) use solvers of evolution PDEs as substitutes for the conventional components in G-CNNs. PDE-G-CNNs can offer several benefits simultaneously: fewer parameters, inherent equivariance, better a
Externí odkaz:
http://arxiv.org/abs/2403.15182
Autor:
Smets, Bart M. N.
These are the lecture notes that accompanied the course of the same name that I taught at the Eindhoven University of Technology from 2021 to 2023. The course is intended as an introduction to neural networks for mathematics students at the graduate
Externí odkaz:
http://arxiv.org/abs/2403.04807
Autor:
Alchorne, Stanesby
Publikováno v:
Philosophical Transactions (1683-1775), 1773 Jan 01. 63, 30-37.
Externí odkaz:
https://www.jstor.org/stable/106133
