Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Rosenbluth, Eran"'
Publikováno v:
The Twelfth International Conference on Learning Representations (2024)
Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal function approximators, with two reservations: 1. The initial node feature
Externí odkaz:
http://arxiv.org/abs/2405.11951
Autor:
Grohe, Martin, Rosenbluth, Eran
Graph neural networks (GNN) are deep learning architectures for graphs. Essentially, a GNN is a distributed message passing algorithm, which is controlled by parameters learned from data. It operates on the vertices of a graph: in each iteration, ver
Externí odkaz:
http://arxiv.org/abs/2403.06817
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Graph Transformers significantly
Externí odkaz:
http://arxiv.org/abs/2309.00367
The expressivity of Graph Neural Networks (GNNs) is dependent on the aggregation functions they employ. Theoretical works have pointed towards Sum aggregation GNNs subsuming every other GNNs, while certain practical works have observed a clear advant
Externí odkaz:
http://arxiv.org/abs/2302.11603
Autor:
Rosenbluth, Eran
Various real-world problems consist of partitioning a set of locations into disjoint subsets, each subset spread in a way that it covers the whole set with a certain radius. Given a finite set S, a metric d, and a radius r, define a subset (of S) S'
Externí odkaz:
http://arxiv.org/abs/2302.03451
Publikováno v:
AIP Conference Proceedings; 2019, Vol. 2126 Issue 1, p030037-1-030037-7, 7p, 6 Color Photographs, 1 Diagram, 1 Graph
