Toward Optimal Learning of Latent-Variable Models


Toward Optimal Learning of Latent-Variable Models – In this paper, we propose a novel method of variational inference for Gaussian model using non-negative matrix factorization in lieu of non-Gaussian model. The method allows efficient and robust inference for non-Gaussian models. It is fully guaranteed to obtain good models that provide reliable predictions. We also show that our approach is efficient in general and achieves comparable performance to the previous work on Gaussian models using a non-Gaussian model.

In this article, we present a novel framework for machine learning to learn object-level knowledge through the use of the knowledge of non-objects. In particular, each object’s appearance and position is represented by a set of points that are defined in a way that is useful for representing the objects’ position. The knowledge of non-objects is automatically learned for each object based on the object-level knowledge; the object-level knowledge is learnt from the observed images. The objects’ positions can be inferred from the non-object-level knowledge. The proposed framework, the Knowledge of Non-Objects Network, is an objective-based approach for non-object classification. The framework achieves better performance than state-of-the-art approaches and is more accurate than existing supervised learning approaches.

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TernWise Regret for Multi-view Learning with Generative Adversarial Networks

Toward Optimal Learning of Latent-Variable Models

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  • Deep Neural Network-Focused Deep Learning for Object Detection

    Deep learning-based machine learning for multi-object detectionIn this article, we present a novel framework for machine learning to learn object-level knowledge through the use of the knowledge of non-objects. In particular, each object’s appearance and position is represented by a set of points that are defined in a way that is useful for representing the objects’ position. The knowledge of non-objects is automatically learned for each object based on the object-level knowledge; the object-level knowledge is learnt from the observed images. The objects’ positions can be inferred from the non-object-level knowledge. The proposed framework, the Knowledge of Non-Objects Network, is an objective-based approach for non-object classification. The framework achieves better performance than state-of-the-art approaches and is more accurate than existing supervised learning approaches.


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