Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel


Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel – The current technique of learning semantic relations in large semantic networks of text is not well suited to solving large, real-time semantic retrieval tasks. The task of semantic relation extraction is a challenging learning problem, and an important one for machine translation. We present a new novel approach to semantic relation extraction that combines deep neural networks (DNNs) with large-scale semantic network models, as well as a novel method to solve the problem. The approach utilizes state-of-the-art deep convolutional networks for the problem and uses them to solve the sentence segmentation task. For the translation tasks, DNNs are used for semantic model learning and for extracting the sentences. Experiments on different datasets show that the method outperforms the state-of-the-art in terms of semantic relation extraction performance and retrieval time.

The problem of predicting faces at different ages from a face database of unknown ages was recently proposed. In such face database, this problem is NP-hard. On the other hand, existing face recognition techniques learn faces by looking for the exact same age in the database. In this paper, we propose a novel approach for face recognition based on the similarity of the different age ranges in a face database to the age range from the database. We propose a novel neural network model that allows the neural network to match the age ranges in the database to the age ranges across a range of ages. After evaluating the accuracy of the model, we propose an algorithm for predicting the age ranges from a database of unknown ages. The proposed approach is tested on a dataset of more than 1000 faces belonging to individuals from North America who are unknown ages at birth. The test model is able to correctly predict the age ranges of 50% of the individuals, and 80% of the individuals.

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Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel

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  • A Survey of Multispectral Image Classification using Gaussian Processes

    An Empirical Analysis on the Validity of Transfer Learning in Face RecognitionThe problem of predicting faces at different ages from a face database of unknown ages was recently proposed. In such face database, this problem is NP-hard. On the other hand, existing face recognition techniques learn faces by looking for the exact same age in the database. In this paper, we propose a novel approach for face recognition based on the similarity of the different age ranges in a face database to the age range from the database. We propose a novel neural network model that allows the neural network to match the age ranges in the database to the age ranges across a range of ages. After evaluating the accuracy of the model, we propose an algorithm for predicting the age ranges from a database of unknown ages. The proposed approach is tested on a dataset of more than 1000 faces belonging to individuals from North America who are unknown ages at birth. The test model is able to correctly predict the age ranges of 50% of the individuals, and 80% of the individuals.


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