3 citations · 7 across the 22 of their papers we have counts for
3 papers · 1 filter
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference
Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò
In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph…
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios
The inductive bias of a graph neural network (GNN) is largely encoded in its specified graph. Latent graph inference relies on latent geometric representations to dynamically rewir…
Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures
Anastasis Kratsios, Ruiyang Hong, Haitz Sáez de Ocáriz Borde
We study the representation capacity of deep hyperbolic neural networks (HNNs) with a ReLU activation function. We establish the first proof that HNNs can -isometrical…