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Explainable Deep Learning: A Field Guide for the Uninitiated
Deep neural network (DNN) is an indispensable machine learning tool for ...
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Contextual Grounding of Natural Language Entities in Images
In this paper, we introduce a contextual grounding approach that capture...
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Visual Entailment: A Novel Task for Fine-Grained Image Understanding
Existing visual reasoning datasets such as Visual Question Answering (VQ...
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Visual Entailment Task for Visually-Grounded Language Learning
We introduce a new inference task - Visual Entailment (VE) - which diffe...
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Reasoning over RDF Knowledge Bases using Deep Learning
Semantic Web knowledge representation standards, and in particular RDF a...
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Relating Input Concepts to Convolutional Neural Network Decisions
Many current methods to interpret convolutional neural networks (CNNs) u...
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Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
The ever increasing prevalence of publicly available structured data on ...
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Ning Xie

I am a 5th year Ph.D. candidate of Computer Science & Engineering at Wright State University, Dayton OH. My research interests are broadly in machine learning, with a focus on explainable and reliable deep learning, computer vision, and interaction between vision and natural languages. My academic research studies deep neural network mechanisms toward interpretability, that aid in interpreting how they process input data in a human-understandable way. This work includes implementation, training, visualization, and analysis of convolutional neural networks. I am also interested in multi-model inference and reasoning and have proposed a novel task Visual Entailment to explore challenging problems on the interaction between vision and natural languages. For more information, please check my homepage at http://www.wright.edu/~xie.25/