activity
20152020
most citedA Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

116 citations · 136 across the 4 of their papers we have counts for

collaborators

7 papers

cs.AI202012 cited

Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess

Nenad Tomašev, Ulrich Paquet, Demis Hassabis +1

It is non-trivial to design engaging and balanced sets of game rules. Modern chess has evolved over centuries, but without a similar recourse to history, the consequences of rule c…

cs.CV2019

Unsupervised Separation of Dynamics from Pixels

Silvia Chiappa, Ulrich Paquet

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way. We introduce a probabilistic model that first generate noisy positions…

stat.ML20181 cited

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…

stat.ML2018

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…

stat.ML2017116 cited

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…

stat.ML2016

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…