15 citations · 45 across the 17 of their papers we have counts for
4 papers · 1 filter
Topological degree as a discrete diagnostic for disentanglement, with applications to the VAE
Mahefa Ratsisetraina Ravelonanosy, Vlado Menkovski, Jacobus W. Portegies
We investigate the ability of Diffusion Variational Autoencoder (VAE) with unit sphere as latent space to capture topological and geometrical structure and disen…
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations
Simon M. Koop, Mark A. Peletier, Jacobus W. Portegies +1
Neural Stochastic Differential Equations (NSDE) have been trained as both Variational Autoencoders, and as GANs. However, the resulting Stochastic Differential Equations can be har…
Universal Approximation in Dropout Neural Networks
Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies +2
We prove two universal approximation theorems for a range of dropout neural networks. These are feed-forward neural networks in which each edge is given a random -valued f…
A Metric for Linear Symmetry-Based Disentanglement
Luis A. Pérez Rey, Loek Tonnaer, Vlado Menkovski +2
The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) outlines the properties that should characterize a disentangled representation tha…