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.

Nonlinear discriminators (NNs) have been widely used for probabilistic inference tasks since the dawn of time. In this work we propose an efficient optimization framework for learning neural networks based on the nonlinearity of Gaussian processes. We show that a supervised learning network that trains on the Gaussian process can outperform the one that does not use it. In particular, we prove that the learned models perform much better in general than the nonlinear discriminators, and we provide a new evaluation metric. The proposed approach yields state-of-the-art results on a large number of benchmark datasets.

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Highly Scalable Latent Semantic Models

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  • Deep Learning for Multi-Person Tracking: An Evaluation

    Learning Discriminative Models from Structured TextNonlinear discriminators (NNs) have been widely used for probabilistic inference tasks since the dawn of time. In this work we propose an efficient optimization framework for learning neural networks based on the nonlinearity of Gaussian processes. We show that a supervised learning network that trains on the Gaussian process can outperform the one that does not use it. In particular, we prove that the learned models perform much better in general than the nonlinear discriminators, and we provide a new evaluation metric. The proposed approach yields state-of-the-art results on a large number of benchmark datasets.


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