199 citations · 301 across the 13 of their papers we have counts for
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cs.NE2014
Improving approximate RPCA with a k-sparsity prior
Maximilian Karl, Christian Osendorfer
A process centric view of robust PCA (RPCA) allows its fast approximate implementation based on a special form o a deep neural network with weights shared across all layers. Howeve…
stat.ML2014★ 199 cited
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…
stat.ML2014★ 2 cited
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…