199 citations · 301 across the 13 of their papers we have counts for
3 papers · 1 filter
Learning Stochastic Recurrent Networks
Justin Bayer, Christian Osendorfer
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model…
Variational inference of latent state sequences using Recurrent Networks
Justin Bayer, Christian Osendorfer
Recent advances in the estimation of deep directed graphical models and recurrent networks let us contribute to the removal of a blind spot in the area of probabilistc modelling of…
On Fast Dropout and its Applicability to Recurrent Networks
Justin Bayer, Christian Osendorfer, Daniela Korhammer +3
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modell…