4 papers
Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems
Kalp Shah, Andrei Vlad Nicula, Mihnea Andrei Bârsan +4
Boundary-value problems for the Laplace equation develop singular behaviour near corners, edges and junctions that smooth approximators resolve poorly. We combine holomorphic neura…
Reducing Memorisation in Generative Models via Riemannian Bayesian Inference
Johanna Marie Gegenfurtner, Albert Kjøller Jacobsen, Naima Elosegui Borras +2
Modern generative models can produce realistic samples, however, balancing memorisation and generalisation remains an open problem. We approach this challenge from a Bayesian persp…
VIKING: Deep variational inference with stochastic projections
Samuel G. Fadel, Hrittik Roy, Nicholas Krämer +5
Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality p…
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery
Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez +2
We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a s…