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cs.LG2020
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Calin Cruceru, Gary Bécigneul, Octavian-Eugen Ganea
Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive bi…
cs.LG2019
Mixed-curvature Variational Autoencoders
Ondrej Skopek, Octavian-Eugen Ganea, Gary Bécigneul
Euclidean geometry has historically been the typical "workhorse" for machine learning applications due to its power and simplicity. However, it has recently been shown that geometr…
cs.LG2019
Constant Curvature Graph Convolutional Networks
Gregor Bachmann, Gary Bécigneul, Octavian-Eugen Ganea
Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain r…