4 papers
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…
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…
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…
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization
Foivos Alimisis, Antonio Orvieto, Gary Bécigneul +1
We propose a novel second-order ODE as the continuous-time limit of a Riemannian accelerated gradient-based method on a manifold with curvature bounded from below. This ODE can be…