Categorization with Linguistic Network and Feature Representation


Categorization with Linguistic Network and Feature Representation – The article provides a new way of learning language semantics and an experimental evaluation on the task of categorization of Chinese vocabulary with the purpose of further understanding its usage in the social domains. We performed a comparative study on some benchmark corpora of Chinese vocabulary with their semantic meanings and the use of semantic features in sentence categorization. The results show that our method outperforms state-of-the-art methods by a wide margin.

In this paper, we propose a new neural network based system for the purpose of unsupervised multi-task classification based on visual-spatial descriptors. The proposed system is a convolutional neural network (CNN) capable of performing well in the task of multi-task classification. The system is trained using a CNN-like architecture and an end-to-end network architecture. The CNN architecture is composed of two subnetworks: one is a single convolutional network that trains the discriminators on top of a convolutional neural network, the other is a distributed module that produces the discriminators on the end. When the CNN architecture is trained, the discriminators of the learned CNN are classified into the task of each individual category. The network is trained on a large dataset of images acquired from the road to demonstrate its effectiveness in unsupervised detection of pedestrians in an urban environment. By using the CNN architecture, the system outperforms the state-of-the-art performance compared to other existing state of the art CNNs on the PASCAL VOC 2015 dataset.

An Empirical Study of Neural Relation Graph Construction for Text Detection

High-Dimensional Scatter-View Covariance Estimation with Outliers

Categorization with Linguistic Network and Feature Representation

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  • Augmented Reality at Scale Using Wavelets and Deep Belief Networks

    A Multi-Task Approach to Unsupervised Mobile Vehicle Detection and Localization Using Visual Cues from Social MediaIn this paper, we propose a new neural network based system for the purpose of unsupervised multi-task classification based on visual-spatial descriptors. The proposed system is a convolutional neural network (CNN) capable of performing well in the task of multi-task classification. The system is trained using a CNN-like architecture and an end-to-end network architecture. The CNN architecture is composed of two subnetworks: one is a single convolutional network that trains the discriminators on top of a convolutional neural network, the other is a distributed module that produces the discriminators on the end. When the CNN architecture is trained, the discriminators of the learned CNN are classified into the task of each individual category. The network is trained on a large dataset of images acquired from the road to demonstrate its effectiveness in unsupervised detection of pedestrians in an urban environment. By using the CNN architecture, the system outperforms the state-of-the-art performance compared to other existing state of the art CNNs on the PASCAL VOC 2015 dataset.


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