Imperial College London Engagement Goal

Recent advances in Deep Content Learning helps support data-driven research at the Data Science Institute at Imperial College London

Use information gathered from academic abstracts to create a reference of individual topics in a Wikipedia type format, in a quick and easy to use format. The main focus is a general framework called Deep Content Learning using two different example datasets. In the traffic prediction project, a new large-scale traffic dataset was released with auxiliary information including search queries from the Baidu Map app. Proposed were hybrid models to achieve state-of-the-art prediction accuracy. The second project involved zero-shot text classification which integrated semantic knowledge and used a two-phase architecture to tackle the challenging zero-shot learning in textual data. In parallel with this research is also their work on TensorFlow. HPCC Systems is perfectly placed to handle the data processing side and delivery of the data. The idea is to use a Thor cluster for the data cleaning, normalization and linking and a ROXIE cluster for the fast delivery of the data to the user. TensorFlow fits snugly in the middle of this workflow, providing the modeling based mainly on recurrent neural networks.