102 citations · 179 across the 11 of their papers we have counts for
6 papers · 1 filter
Continuous normalizing flows on manifolds
Luca Falorsi
Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions. Unfortunately, current approaches are only available for t…
Neural Ordinary Differential Equations on Manifolds
Luca Falorsi, Patrick Forré
Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions. Unfortunately current approaches fall short when the under…
Reparameterizing Distributions on Lie Groups
Luca Falorsi, Pim de Haan, Tim R. Davidson +1
Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradien…
Topological Constraints on Homeomorphic Auto-Encoding
Pim de Haan, Luca Falorsi
When doing representation learning on data that lives on a known non-trivial manifold embedded in high dimensional space, it is natural to desire the encoder to be homeomorphic whe…
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi, Pim de Haan, Tim R. Davidson +4
The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a co…
Hyperspherical Variational Auto-Encoders
Tim R. Davidson, Luca Falorsi, Nicola De Cao +2
The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and p…