Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Cantürk, Semih"'
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (and machine learning more generally) to solve combinatorial optimization
Externí odkaz:
http://arxiv.org/abs/2405.20543
Autor:
Cantürk, Semih
The core research of this thesis, mostly comprising chapter four, has been accepted to the Learning on Graphs (LoG) 2022 conference for a spotlight presentation as a standalone paper, under the title "Taxonomy of Benchmarks in Graph Representation Le
Autor:
Cantürk, Semih, Liu, Renming, Lapointe-Gagné, Olivier, Létourneau, Vincent, Wolf, Guy, Beaini, Dominique, Rampášek, Ladislav
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for all grap
Externí odkaz:
http://arxiv.org/abs/2307.07107
Autor:
Liu, Renming, Cantürk, Semih, Wenkel, Frederik, McGuire, Sarah, Wang, Xinyi, Little, Anna, O'Bray, Leslie, Perlmutter, Michael, Rieck, Bastian, Hirn, Matthew, Wolf, Guy, Rampášek, Ladislav
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collectio
Externí odkaz:
http://arxiv.org/abs/2206.07729
Autor:
Liu, Renming, Cantürk, Semih, Wenkel, Frederik, Sandfelder, Dylan, Kreuzer, Devin, Little, Anna, McGuire, Sarah, O'Bray, Leslie, Perlmutter, Michael, Rieck, Bastian, Hirn, Matthew, Wolf, Guy, Rampášek, Ladislav
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN models have been developed, with many benchmarking procedures to demonst
Externí odkaz:
http://arxiv.org/abs/2110.14809
The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel pathology. We aim to discover t
Externí odkaz:
http://arxiv.org/abs/2006.14707
Autor:
Liu, Renming, Cantürk, Semih, Lapointe-Gagné, Olivier, Létourneau, Vincent, Wolf, Guy, Beaini, Dominique, Rampášek, Ladislav
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54e962c902eb252b65fb97ac9eed7300
http://arxiv.org/abs/2307.07107
http://arxiv.org/abs/2307.07107