Zobrazeno 1 - 10
of 25 311
pro vyhledávání: '"Levie, A."'
We analyze the universality and generalization of graph neural networks (GNNs) on attributed graphs, i.e., with node attributes. To this end, we propose pseudometrics over the space of all attributed graphs that describe the fine-grained expressivity
Externí odkaz:
http://arxiv.org/abs/2411.05464
Autor:
VAN OS, HENK
Publikováno v:
The Rijksmuseum Bulletin, 2016 Jan 01. 64(4), 398-399.
Externí odkaz:
https://www.jstor.org/stable/44028837
Autor:
Greenwald, Ted
Publikováno v:
MIT Technology Review. Jan/Feb2014, Vol. 117 Issue 1, p46-51. 6p. 4 Color Photographs, 1 Illustration.
Autor:
DAVIDSSON, PER1
Publikováno v:
Academy of Management Discoveries. Jun2021, Vol. 7 Issue 2, p317-319. 3p.
Autor:
Zilberg, Daniel, Levie, Ron
We propose PieClam (Prior Inclusive Exclusive Cluster Affiliation Model): a probabilistic graph model for representing any graph as overlapping generalized communities. Our method can be interpreted as a graph autoencoder: nodes are embedded into a c
Externí odkaz:
http://arxiv.org/abs/2409.11618
Autor:
Konrad, Alex
Publikováno v:
Forbes. 1/18/2016, Vol. 197 Issue 1, p82-82. 2/7p. 1 Color Photograph.
Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving generalization. In this paper, we focus on an extension of shift equiva
Externí odkaz:
http://arxiv.org/abs/2406.01249
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates a memory complexity of the ord
Externí odkaz:
http://arxiv.org/abs/2405.20724
We study the generalization capabilities of Message Passing Neural Networks (MPNNs), a prevalent class of Graph Neural Networks (GNN). We derive generalization bounds specifically for MPNNs with normalized sum aggregation and mean aggregation. Our an
Externí odkaz:
http://arxiv.org/abs/2404.03473
Autor:
Barret, Victoria
Publikováno v:
Forbes. 2/14/2011, Vol. 187 Issue 2, p34-36. 3p. 1 Color Photograph.