2 papers
cs.HC2026
Inductive Graph Layout with Implicit Neural Fields
Berfin Inal, Daniel Probst
A graph layout is normally a table of free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sample complexity and an extens…
cs.LG2025
Connecting Neural Models Latent Geometries with Relative Geodesic Representations
Hanlin Yu, Berfin Inal, Georgios Arvanitidis +3
Neural models learn representations of high-dimensional data on low-dimensional manifolds. Multiple factors, including stochasticities in the training process, model architectures,…