Search Results for author:
Found 7 papers, 5 papers with code
Sequential Neural Models with Stochastic Layers
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks?
Auxiliary Deep Generative Models
The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive.
Ranked #49 on
Image Classification
on SVHN
Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning.
Autoencoding beyond pixels using a learned similarity metric
We present an autoencoder that leverages learned representations to better measure similarities in data space.
Recurrent Spatial Transformer Networks
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.
Convolutional LSTM Networks for Subcellular Localization of Proteins
Machine learning is widely used to analyze biological sequence data.
Protein Secondary Structure Prediction with Long Short Term Memory Networks
Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data.
