#### Problem addressed: 
Generate images with recurrent neural networks

#### Summary: 
This paper propose an architecture for image generation. The model itself is similar to variational autoencoder, but both the encoder and decoder are implemented with recurrent neural networks, in particular LSTM. It also has two new components: 

1. a reader that select an area of interest for the next recurrence and 

2. a writer that write to that particular area. They believe this mimics the attention and demonstrated this on a cluttered mnist dataset.

#### Novelty:
Using RNNs for image generation and selective attention of the region of interest

#### Drawbacks:
Idea seems over complicated and the image generation performance is not that good on real image datset such as CIFAR.

#### Datasets:
MNIST, SVHN, CIFAR

#### Additional remarks:
Presentation video available on cedar server

#### Presenter:
Yingbo Zhou