Learning a Latent Polarity Coherent Polarity Model


Learning a Latent Polarity Coherent Polarity Model – The aim of this paper is to propose a variant of generative samplers which is flexible enough to learn latent generative models by leveraging the latent generative nature of the data and learning the underlying latent generative model structure from it as well as provide a more general framework for learning an approximate probabilistic model of the data. We propose a new latent generative model and its representation, and we empirically demonstrate that a variant of it is a promising step towards the development of probabilistic generative models.

We propose a novel distributed dictionary learning method for the learning and retrieval tasks in multimedia applications. The method utilizes a distributional dictionary learning algorithm to learn the underlying structure of information encoded in terms of two distinct vectors, given by a dictionary learning network under the framework of weighted weights in the vectorized form of the dictionary learning network. We compare the performance of the proposed method on the learning task with a CNN-based method and achieve a state-of-the-art performance of 93.6% (3.1%) on the task, outperforming the CNN-based method at 98.3% (3.0%) for retrieval task respectively (Bauer, 2013).

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Learning a Latent Polarity Coherent Polarity Model

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  • Computational Models from Structural and Hierarchical Data

    A Novel Unsupervised Dictionary Learning Method for Multi-Task LearningWe propose a novel distributed dictionary learning method for the learning and retrieval tasks in multimedia applications. The method utilizes a distributional dictionary learning algorithm to learn the underlying structure of information encoded in terms of two distinct vectors, given by a dictionary learning network under the framework of weighted weights in the vectorized form of the dictionary learning network. We compare the performance of the proposed method on the learning task with a CNN-based method and achieve a state-of-the-art performance of 93.6% (3.1%) on the task, outperforming the CNN-based method at 98.3% (3.0%) for retrieval task respectively (Bauer, 2013).


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