156 citations · 440 across the 15 of their papers we have counts for
4 papers · 1 filter
The Bayesian Low-Rank Determinantal Point Process Mixture Model
Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are usef…
Sequential Neural Models with Stochastic Layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet +1
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue…
An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants
Marco Fraccaro, Ulrich Paquet, Ole Winther
The estimation of normalizing constants is a fundamental step in probabilistic model comparison. Sequential Monte Carlo methods may be used for this task and have the advantage of…
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…