8 citations · 18 across the 9 of their papers we have counts for
6 papers · 1 filter
Hyperbolic Graph Representation Learning: Embed in One Metric, Optimize with Another
Federico Larroca, Paola Bermolen, Marcelo Fiori +1
Hierarchical graphs embed in hyperbolic space with lower distortion than in Euclidean space owing to its negative curvature. However, their gradient-based learning is hampered at l…
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset
Ignacio Boero, Santiago Diaz, Tomás Vázquez +3
The Optimal Reactive Power Dispatch (ORPD) problem plays a crucial role in power system operations, ensuring voltage stability and minimizing power losses. Recent advances in machi…
Graph Contrastive Learning for Connectome Classification
Martín Schmidt, Sara Silva, Federico Larroca +2
With recent advancements in non-invasive techniques for measuring brain activity, such as magnetic resonance imaging (MRI), the study of structural and functional brain networks th…
LASE: Learned Adjacency Spectral Embeddings
Sofía Pérez Casulo, Marcelo Fiori, Federico Larroca +1
We put forth a principled design of a neural architecture to learn nodal Adjacency Spectral Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method…
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
Marcelo Fiori, Bernardo Marenco, Federico Larroca +2
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially po…
Online Change Point Detection for Weighted and Directed Random Dot Product Graphs
Bernardo Marenco, Paola Bermolen, Marcelo Fiori +2
Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to…