16 citations · 20 across the 2 of their papers we have counts for
13 papers
Message Passing Networks for Molecules with Tetrahedral Chirality
Lagnajit Pattanaik, Octavian-Eugen Ganea, Ian Coley +3
Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic. However, m…
Hierarchical Image Classification using Entailment Cone Embeddings
Ankit Dhall, Anastasia Makarova, Octavian Ganea +3
Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training.…
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…
Noise Contrastive Variational Autoencoders
Octavian-Eugen Ganea, Yashas Annadani, Gary Bécigneul
We take steps towards understanding the "posterior collapse (PC)" difficulty in variational autoencoders (VAEs),~i.e. a degenerate optimum in which the latent codes become independ…