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.

We propose an unsupervised algorithm to predict the location of a node in a graph by means of a hidden Markov model. We propose a method for estimating the location of a node using Gaussian Processes based on two types of prior knowledge: (1) the prior knowledge used to estimate the node and the posterior information to infer its posterior; (2) the posterior information used to estimate the node’s location using Markov networks, a general purpose model that assumes that the node’s location is local to the center of the graph. More specifically, by estimating the prior and posterior knowledge of a node with respect to a tree, we design a linear sparse model that considers the tree as a prior over nodes, and uses it in order to estimate the node’s position. Since the prior and posterior information for nodes are local to each other, the node’s location can be estimated in the non-parametric manner via the tree. We present experimental results showing that the proposed method outperforms the state-of-the-art methods on several benchmark datasets.

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Multitask Learning for Knowledge Base Linking via Neural-Synthesis

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  • Ranking from Observational Data by Using Bags

    Non-parametric Inference for Mixed Graphical ModelsWe propose an unsupervised algorithm to predict the location of a node in a graph by means of a hidden Markov model. We propose a method for estimating the location of a node using Gaussian Processes based on two types of prior knowledge: (1) the prior knowledge used to estimate the node and the posterior information to infer its posterior; (2) the posterior information used to estimate the node’s location using Markov networks, a general purpose model that assumes that the node’s location is local to the center of the graph. More specifically, by estimating the prior and posterior knowledge of a node with respect to a tree, we design a linear sparse model that considers the tree as a prior over nodes, and uses it in order to estimate the node’s position. Since the prior and posterior information for nodes are local to each other, the node’s location can be estimated in the non-parametric manner via the tree. We present experimental results showing that the proposed method outperforms the state-of-the-art methods on several benchmark datasets.


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