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 propose a novel algorithm for recognizing human actions in videos using the deep convolutional neural network (CNN) in a low rank dimension setting. The proposed algorithm aims at recognizing multiple human actions within a single image. The new CNN model is built using convolutional neural network (CNN) feature and then the data is collected to extract individual actions. The network produces global object detection and segmentation scores of different human actions in a sequence and then the network takes advantage of the visual features extracted from the entire sequence to learn their local actions. The network is trained to recognize human actions with no supervision, which is an improvement over prior works. In contrast to previous works, both CNN and the CNN feature learning is applied in supervised manner and the features extracted from the whole sequence are used to identify the local actions. Moreover, the CNN features were pre-trained to be able to discriminate the actions on the test set. Results shows that the proposed CNN model can be used for real-time action recognition.

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Learning the Top Labels of Short Texts for Spiny Natural Words

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  • Unsupervised Learning from Analogue Videos via Meta-Learning

    An Online Corpus of Electronic Medical RecordsIn this paper, we propose a novel algorithm for recognizing human actions in videos using the deep convolutional neural network (CNN) in a low rank dimension setting. The proposed algorithm aims at recognizing multiple human actions within a single image. The new CNN model is built using convolutional neural network (CNN) feature and then the data is collected to extract individual actions. The network produces global object detection and segmentation scores of different human actions in a sequence and then the network takes advantage of the visual features extracted from the entire sequence to learn their local actions. The network is trained to recognize human actions with no supervision, which is an improvement over prior works. In contrast to previous works, both CNN and the CNN feature learning is applied in supervised manner and the features extracted from the whole sequence are used to identify the local actions. Moreover, the CNN features were pre-trained to be able to discriminate the actions on the test set. Results shows that the proposed CNN model can be used for real-time action recognition.


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