Highly Scalable Latent Semantic Models

Highly Scalable Latent Semantic Models – This paper focuses on learning models for latent semantic models of natural language. We assume that the model has a set of semantic instances along with a model representation, which are stored in an associative memory unit, called RKU. RKU is a structured data representation, which can be applied to any neural network. However, it is only feasible for the model to represent data with small-sample data, even for supervised learning. We propose a new representation of RKU structure for language models that can be computed efficiently by learning RKU structures. A model for RKU structures can be learned efficiently using state-of-the-art deep learning techniques. We show that in real applications, an RKU structure can be learned to generate syntactic labels.

This paper proposes a new nonparametric method for the clustering of objects in a sequence of high-dimensional data. The method uses the maximum of all variables in a sequence as the clustering metric and then the clusters are clustered using the best clustering criterion. This method is compared with several existing methods that use multiple feature maps, and an innovative data-parallelism method has been incorporated to the clustering. The experimental results demonstrate that the proposed method significantly outperforms the existing methods.

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Highly Scalable Latent Semantic Models

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    Online Learning of Hierarchical Classification ModelsThis paper proposes a new nonparametric method for the clustering of objects in a sequence of high-dimensional data. The method uses the maximum of all variables in a sequence as the clustering metric and then the clusters are clustered using the best clustering criterion. This method is compared with several existing methods that use multiple feature maps, and an innovative data-parallelism method has been incorporated to the clustering. The experimental results demonstrate that the proposed method significantly outperforms the existing methods.


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