M. Shoaib Malik^{1, 2} and Dagmar Waltemath¹, ¹University Medicine Greifswald, Germany, ²Air University, Pakistan

A Deep Neural Network (DNN) can be used to learn higher-level and more abstract representations of a particular input. DNNs have successfully been applied to analysis tasks including image processing, unsupervised feature learning, and natural language processing. DNNs furthermore can improve computing performance when compared to shallower networks, for example in pattern recognition tasks in machine learning. Recent usage of DNNs in search engines for the Web have impacted that technology in industrial scale applications. One example for such an application is deepgif - a search engine for Graphics Interchange Format (GIF) images that is based on a convolutional neural network and takes natural language text as query. In this study, we developed a tool and compared the performance of feed-forward neural networks and deep architectures of recurrent neural network using the case of document retrieval. This study first discusses two architectural setups used to build the models and then provide a detailed comparison of their performance. The goal is to identify the architecture that is most suited for the task of document retrieval.

Deep Neural Network, Machine Learning, Document Retrieval, Feed-Forward Neural Network, Recurrent Neural Network.