Learning Feature Representations with Graphs: The Power of Variational Inference


Learning Feature Representations with Graphs: The Power of Variational Inference – The success of deep neural networks can be attributed to their ability to discover more complex structures than existing ones due to its ability to extract useful local information. This paper considers the use of such data to design features of data structures. In this framework, the learning problem is formulated as a non-distributed tree-structured graph and its output is a function of the graph’s structure. This structure is used in the learning task to extract information about the network structure. To illustrate this concept, this research aims at developing a probabilistic parser for the tree-structured graph.

We propose an efficient and robust optimization algorithm for training Bayesian networks. We show several theoretical bounds on the Bayesian framework. Our algorithm is competitive with the state-of-the-art approaches and outperforms them. Moreover, we show how other methods, including the ones used in the literature, can be improved.

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Learning Feature Representations with Graphs: The Power of Variational Inference

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  • Bayesian Models for Non-convex Low Rank Problems

    Learning Graphs from Continuous Time and Space VariablesWe propose an efficient and robust optimization algorithm for training Bayesian networks. We show several theoretical bounds on the Bayesian framework. Our algorithm is competitive with the state-of-the-art approaches and outperforms them. Moreover, we show how other methods, including the ones used in the literature, can be improved.


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