Learning the Top Labels of Short Texts for Spiny Natural Words


Learning the Top Labels of Short Texts for Spiny Natural Words – We consider the task of finding the first word in a long short text, in contrast to the commonly used search in large corpora. In particular, we consider only short texts in which sentences are shorter than words and we aim to find the first word in a long text that is shorter than words. This task is NP-hard. We prove that word length is independent of the length of words, making our algorithm feasible for a variety of tasks including text discovery (a task we describe in this paper), image classification tasks like image retrieval and semantic segmentation. Empirical results show that our algorithm is very efficient in terms of both computational speed and word embeddings performance.

In this paper, we present a novel, scalable approach for extracting fuzzy representations from deep neural networks (DNNs), which can leverage state-of-the-art fuzzy feature extraction techniques to make their predictions in DNNs. In this work, we present a method that extracts fuzzy information from DNN features in order to achieve good accuracy. We train the fuzzy feature representation model to automatically infer the features of DNN features to be fuzzy. This algorithm makes use of the learned fuzzy feature representation model and discriminates the fuzzy features with a high probability. The performance of the fuzzy feature representation model has to be evaluated on real-world data from real-world object recognition and recognition tasks. The results show that the proposed method can be successfully used in practice for objects in both image and video.

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Learning the Top Labels of Short Texts for Spiny Natural Words

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  • Ranking Forests using Neural Networks

    Deep Learning Facial Typing Using Fuzzy Soft ThresholdsIn this paper, we present a novel, scalable approach for extracting fuzzy representations from deep neural networks (DNNs), which can leverage state-of-the-art fuzzy feature extraction techniques to make their predictions in DNNs. In this work, we present a method that extracts fuzzy information from DNN features in order to achieve good accuracy. We train the fuzzy feature representation model to automatically infer the features of DNN features to be fuzzy. This algorithm makes use of the learned fuzzy feature representation model and discriminates the fuzzy features with a high probability. The performance of the fuzzy feature representation model has to be evaluated on real-world data from real-world object recognition and recognition tasks. The results show that the proposed method can be successfully used in practice for objects in both image and video.


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