Interactive Online Learning


Interactive Online Learning – A variety of methods for learning natural language have been proposed to solve problems of learning the semantic knowledge. However, existing methods usually neglect the semantics of the language and they are not relevant to many tasks beyond human-computer interaction. In this paper we first outline a novel approach for learning natural language using a fully neural network architecture based semantic parsing system. The representation learned from the network is then used to optimize the semantic representation for each language. More specifically, the semantic parsing of a language is obtained by integrating two sub-words of the same language into it. In the present work, we focus on the semantic parsing of English which was used to perform the first part of this model. The semantic parsing is trained over two years with a model which learned from raw English texts. We show that all the proposed approaches converge to the semantic parser using less time (10x less computation) and higher accuracy than those with more complex models.

We present a novel methodology for training deep Convolutional neural networks, in which the network is trained from two images to a single image. Different from image learning, our approach addresses problems with image retrieval from an unsupervised learning standpoint, which requires both training data and an image representation which is robust to variations in the training set, and also performs the learning in an unsupervised way. We show that the learned image representation can be used to guide the recurrent network architecture, in order to efficiently train. A supervised model can be trained for each image of the same image, and the resulting model learns the image representation in an unsupervised manner, for example by performing segmentation and restoration in an unsupervised manner. We also propose to use an image representation for the model, allowing to recover from the training data only the image in which a deep recurrent network is trained, and the trained model is trained in a supervised way. We demonstrate that robust image retrieval results are achieved using the supervised architecture and the image representation, and we also demonstrate that the neural network architecture outperforms and surpasses a traditional method for image retrieval.

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Interactive Online Learning

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  • Identifying the most relevant regions in large-scale radiocarbon age assessment

    Image Segmentation and Reconstruction using Deep Convolutional Neural NetworksWe present a novel methodology for training deep Convolutional neural networks, in which the network is trained from two images to a single image. Different from image learning, our approach addresses problems with image retrieval from an unsupervised learning standpoint, which requires both training data and an image representation which is robust to variations in the training set, and also performs the learning in an unsupervised way. We show that the learned image representation can be used to guide the recurrent network architecture, in order to efficiently train. A supervised model can be trained for each image of the same image, and the resulting model learns the image representation in an unsupervised manner, for example by performing segmentation and restoration in an unsupervised manner. We also propose to use an image representation for the model, allowing to recover from the training data only the image in which a deep recurrent network is trained, and the trained model is trained in a supervised way. We demonstrate that robust image retrieval results are achieved using the supervised architecture and the image representation, and we also demonstrate that the neural network architecture outperforms and surpasses a traditional method for image retrieval.


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