4 papers
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…
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,…
Adaptive Sampling for Continuous Group Equivariant Neural Networks
Berfin Inal, Gabriele Cesa
Steerable networks, which process data with intrinsic symmetries, often use Fourier-based nonlinearities that require sampling from the entire group, leading to a need for discreti…
DC3DO: Diffusion Classifier for 3D Objects
Nursena Koprucu, Meher Shashwat Nigam, Shicheng Xu +6
Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification.…