116 citations · 136 across the 4 of their papers we have counts for
5 papers · 1 filter
A Factorial Mixture Prior for Compositional Deep Generative Models
Ulrich Paquet, Sumedh K. Ghaisas, Olivier Tieleman
We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its p…
An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models
Ulrich Paquet, Marco Fraccaro
This technical report presents pseudo-code for a Riemannian manifold Hamiltonian Monte Carlo (RMHMC) method to efficiently simulate samples from -dimensional posterior distribut…
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet +1
This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non…
Low-Rank Factorization of Determinantal Point Processes for Recommendation
Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including p…
On the Convergence of Stochastic Variational Inference in Bayesian Networks
Ulrich Paquet
We highlight a pitfall when applying stochastic variational inference to general Bayesian networks. For global random variables approximated by an exponential family distribution,…