Multitask Learning for Knowledge Base Linking via Neural-Synthesis


Multitask Learning for Knowledge Base Linking via Neural-Synthesis – In this paper, we propose a novel method of inferring the model parameters given the data which is based on deep learning. We show that deep learning based models have significantly improved state-of-the-art classification accuracy, with a significant reduction in classification time. Also, deep learning based models outperform state-of-the-art methods that use hand-coded attributes. This paper gives us an opportunity to evaluate the proposed approach for various tasks like human face recognition, social interaction, etc. In experiments involving humans, we find that there is a significant reduction in the number of features which is due to the use of deep learning models and a real-time feature extraction approach.

Recently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.

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Multitask Learning for Knowledge Base Linking via Neural-Synthesis

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  • Mixed-Membership Recursive Partitioning

    Fast Low-Rank Matrix Estimation for High-Dimensional Text ClassificationRecently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.


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