Applications of Deep Representation Learning to Natural Language Processing and Satellite Imagery

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2020

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Abstract

Deep representation learning has shown its effectiveness in many tasks such as text classification and image processing. Many researches have been done to directly improve the representation quality. However, how to improve the representation quality by cooperating ancillary data source or by interacting with other representations is still not fully explored. Also, using representation learning to help other tasks is worth further exploration.

In this work, we explore these directions by solving various problems in natural language processing and image processing. In the natural language processing part, we first discuss how to introduce alternative representations to improve the original representation quality and hence boost the model performance. We then discuss a text representation matching algorithm. By introducing such matching algorithm, we can better align different text representations in text generation models and hence improve the generation qualities.

For the image processing part, we consider a real-world air condition prediction problem: ground-level $PM_{2.5}$ estimation. To solve this problem, we introduce a joint model to improve image representation learning by incorporating image encoder with ancillary data source and random forest model. We the further extend this model with ranking information for semi-supervised learning setup. The semi-supervised model can then utilize low-cost sensors for $PM_{2.5}$ estimation.

Finally, we introduce a recurrent kernel machine concept to explain the representation interaction mechanism within time-dependent neural network models and hence unified a variety of algorithms into a generalized framework.

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Wang, Guoyin (2020). Applications of Deep Representation Learning to Natural Language Processing and Satellite Imagery. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/20941.

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Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.