Universal Dependency-Aware Knowledge Base Completion


Universal Dependency-Aware Knowledge Base Completion – We demonstrate how to build an intelligent agent that learns from its environment to perform well in the real world. We propose an effective and complete approach for this task, and show how it is learned and deployed for learning, a very important capability for any intelligent agent.

In this paper, a new type of sparse representation for visual semantic object classification based on similarity is proposed. The proposed representation is based on the use of two-dimensional representation of visual information by a low level memory unit (memory architecture), and uses such representation (memory architecture) to build a set of semantic structures. We apply the proposed approach to semantic segmentation and retrieval. The proposed representation is achieved by combining the two-dimensional representation representation with the low level memory representation and using such representation to construct a model. Our experiments show the proposed approach is superior to the state-of-the-art semantic segmentation and retrieval methods.

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Universal Dependency-Aware Knowledge Base Completion

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  • A Bayesian Network Architecture for Multi-Modal Image Search, Using Contextual Tasks

    Spacetimes in the Brain: A Brain-Inspired Approach to Image Retrieval and Text AnalysisIn this paper, a new type of sparse representation for visual semantic object classification based on similarity is proposed. The proposed representation is based on the use of two-dimensional representation of visual information by a low level memory unit (memory architecture), and uses such representation (memory architecture) to build a set of semantic structures. We apply the proposed approach to semantic segmentation and retrieval. The proposed representation is achieved by combining the two-dimensional representation representation with the low level memory representation and using such representation to construct a model. Our experiments show the proposed approach is superior to the state-of-the-art semantic segmentation and retrieval methods.


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