Learning Representations from Knowledge Graphs


Learning Representations from Knowledge Graphs – We propose a novel framework for learning structured models of action-action interactions. The framework is based on the recent work of Yap and Chiao (2009) of learning structured models of action-action interfaces. In a supervised domain, a deep network is trained to be able to model the interaction of user-defined actions and objects, and then the model is extended to learn actions or objects independently. This framework learns the interactions of multiple users and interactions, and the interaction model is then modeled on the interaction space of the user-defined actions and objects. We show how to use the framework for learning structured action models from action spaces in a setting where the user has limited amount of knowledge. As a case study, we experimentally demonstrate the usefulness of the proposed system. The method is able to learn an agent from a knowledge graph, and the knowledge graph is then used to model the interaction between the agent and the user model.

We propose an effective and robust method for the face recognition task at hand. It extends the approach from the face recognition task of a user to that of a human. Our method uses a deep neural network to discover the region of interest and the global context of the region. Unlike previous approaches for recognizing faces in images, we focus on the region of interest in order to learn how to predict the identity function in the region. Our method learns the global context by learning a new face identity function that maps a set of the face instances together. We use this new identity function to predict the pose for a given face instance as well as pose prediction metrics such as SVMs. Our method outperforms state-of-the-art human-level face recognition methods on the BLEU dataset with an accuracy of 97% that is comparable to human experts, which are more challenging to achieve in the face recognition community.

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Learning Representations from Knowledge Graphs

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  • Deep learning in the wild: a computational perspective

    Deep Learning Approach to Robust Face Recognition in Urban EnvironmentWe propose an effective and robust method for the face recognition task at hand. It extends the approach from the face recognition task of a user to that of a human. Our method uses a deep neural network to discover the region of interest and the global context of the region. Unlike previous approaches for recognizing faces in images, we focus on the region of interest in order to learn how to predict the identity function in the region. Our method learns the global context by learning a new face identity function that maps a set of the face instances together. We use this new identity function to predict the pose for a given face instance as well as pose prediction metrics such as SVMs. Our method outperforms state-of-the-art human-level face recognition methods on the BLEU dataset with an accuracy of 97% that is comparable to human experts, which are more challenging to achieve in the face recognition community.


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