DeepLung: Deep Neural Networks for Deep Disentangling


DeepLung: Deep Neural Networks for Deep Disentangling – In this paper, we demonstrate a new algorithm to transform unlabeled text in several sentences to extract a representation of the content of the sentences and, if required, to generate a new sentence. This is achieved by incorporating hidden meanings in the text. Our experiments demonstrate that the proposed method outperforms state-of-the-art supervised text analysis methods on standard benchmark word embeddings by several orders of magnitude, while requiring minimal human annotations.

In this paper, we proposed a new algorithm for the automatic classification of complex, structured, and unordered data sets. We first show that the proposed approach works well when the data set is a set of labels, and only for labels with a probability lower than the distribution of labeled data. We then show that the proposed approach makes no assumptions on labels, and thus may be useful for models which are restricted to labels at the label level for classification purposes. We show that the proposed algorithm has many important advantages over its competitors.

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DeepLung: Deep Neural Networks for Deep Disentangling

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    Machine Learning with the Roto-Margin Tree TechniqueIn this paper, we proposed a new algorithm for the automatic classification of complex, structured, and unordered data sets. We first show that the proposed approach works well when the data set is a set of labels, and only for labels with a probability lower than the distribution of labeled data. We then show that the proposed approach makes no assumptions on labels, and thus may be useful for models which are restricted to labels at the label level for classification purposes. We show that the proposed algorithm has many important advantages over its competitors.


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