Efficient Learning on a Stochastic Neural Network – The state-of-the-art recurrent neural encoder model (RNN) is a popular way to learn a rich set of visual objects in order to generate large amounts of data. However, it is still the case that deep neural networks (DNNs) do not directly represent the object representation. In this paper, we show how to generate a deep RNN by transforming an existing one into a model of the object representation. In addition, we show that this transformation could be used to train a model by leveraging the fact that a deep DNN can be trained so that its training volume is comparable to the input image or the corresponding dataset. This experiment is carried out on the MNIST dataset and we show that our model generates better results than an existing deep DNN model.

The goal is to use deep learning approach to automatically produce high quality images from a large database. But there are many reasons why it is not easy to do so. In this paper, we propose a novel approach to create a large-scale, low-cost image retrieval using multi-view semantic segmentation. Our architecture consists of two main components: (1) a robustly model-driven deep neural network (DRNN) module, (2) an image captioning approach that simultaneously learns a semantic model of the underlying image. Extensive experiments were conducted on the COCO dataset to show that our approach achieves state-of-the-art retrieval performance on a huge set of images.

Avalon: Towards a Database to Generate Traditional Arabic Painting Instructions

End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks

Efficient Learning on a Stochastic Neural Network

A new type of syntactic constant applied to language structures

Towards Knowledge Based Image RetrievalThe goal is to use deep learning approach to automatically produce high quality images from a large database. But there are many reasons why it is not easy to do so. In this paper, we propose a novel approach to create a large-scale, low-cost image retrieval using multi-view semantic segmentation. Our architecture consists of two main components: (1) a robustly model-driven deep neural network (DRNN) module, (2) an image captioning approach that simultaneously learns a semantic model of the underlying image. Extensive experiments were conducted on the COCO dataset to show that our approach achieves state-of-the-art retrieval performance on a huge set of images.