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In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results , but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit(More)
We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the final summary. The Selector architecture, on the other hand, is free to pick one sentence(More)
Deep learning techniques are increasingly popular in the textual entailment task, overcoming the fragility of traditional discrete models with hard alignments and logics. In particular, the recently proposed attention models (Rocktäschel et al., 2015; Wang and Jiang, 2015) achieves state-of-the-art accuracy by computing soft word alignments between the(More)
In this paper we present a novel generalization of Sammon's Mapping (SM), which is a popular, metric multi-dimensional scaling technique used in data analysis and visualization. The new approach, namely the Kernel-based Sammon Mapping (KSM), yields the classic SM and other much related techniques as special cases. Apart from being able to approximate(More)
— Recognizing human face from image set has recently seen its prosperity because of its effectiveness in dealing with variations in illumination, expressions, or poses. In this paper, inspired by the prototype notion originating from cognition field, we obtain discriminative feature representation for face recognition by implementing prototype formation on(More)
Machine learning techniques are widely used in the domain of Natural Language Processing (NLP) and Computer Vision (CV), In order to capture complex and non-linear features deeper machine learning architectures become more and more popular. A lot of the state of art performance have been reported by employing deep learning techniques. Convolutional Neural(More)
Question classification is an important task with wide applications. However, traditional techniques treat questions as general sentences, ignoring the corresponding answer data. In order to consider answer information into question modeling, we first introduce novel group sparse autoencoders which refine question representation by utilizing group(More)