116 citations · 116 across the 1 of their papers we have counts for
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BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Lars Maaløe, Marco Fraccaro, Valentin Liévin +1
With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their perfo…
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…
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols +3
In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations duri…
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…