1 code implementation • NeurIPS 2016 • Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks?
1 code implementation • 17 Feb 2016 • Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther
The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive.
Ranked #49 on Image Classification on SVHN
5 code implementations • NeurIPS 2016 • Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther
Variational Autoencoders are powerful models for unsupervised learning.
28 code implementations • 31 Dec 2015 • Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther
We present an autoencoder that leverages learned representations to better measure similarities in data space.
2 code implementations • 17 Sep 2015 • Søren Kaae Sønderby, Casper Kaae Sønderby, Lars Maaløe, Ole Winther
We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance.
no code implementations • 6 Mar 2015 • Søren Kaae Sønderby, Casper Kaae Sønderby, Henrik Nielsen, Ole Winther
Machine learning is widely used to analyze biological sequence data.
no code implementations • 25 Dec 2014 • Søren Kaae Sønderby, Ole Winther
Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data.