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.

This paper addresses the problem of learning a fully convolutional, multi-scale learning framework for multiple images with different aspects and settings. In this paper, we propose a new method for learning the feature maps from multiple images of the same object using different dimensions. Our method, which has been optimized at the level of the feature maps, is able to learn the semantic information of the 3D part of the object. We evaluate the learned model on a set of 4 different object images and compared it with a baseline method that trained only with 1.6 units. We test the proposed method on 3D part prediction and classification tasks such as classification on RGB images and segmentation of 3D object pairs. The proposed method demonstrated highly competitive performance compared with the baseline method.

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Learning Representations from Knowledge Graphs

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    Deep Multi-Scale Multi-Task Learning via Low-rank Representation of 3D Part FramesThis paper addresses the problem of learning a fully convolutional, multi-scale learning framework for multiple images with different aspects and settings. In this paper, we propose a new method for learning the feature maps from multiple images of the same object using different dimensions. Our method, which has been optimized at the level of the feature maps, is able to learn the semantic information of the 3D part of the object. We evaluate the learned model on a set of 4 different object images and compared it with a baseline method that trained only with 1.6 units. We test the proposed method on 3D part prediction and classification tasks such as classification on RGB images and segmentation of 3D object pairs. The proposed method demonstrated highly competitive performance compared with the baseline method.


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