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      Twitter User Geolocation using Deep Multiview Learning

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          Abstract

          Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the latest advances in deep learning and multiview learning. A realization of MENET with textual, network and metadata features results in an effective method for Twitter user geolocation, achieving the state of the art on two well-known datasets.

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          Speech Recognition with Deep Recurrent Neural Networks

          Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates \emph{deep recurrent neural networks}, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.
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            Multi-view learning overview: Recent progress and new challenges

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              On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification

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                Author and article information

                Journal
                11 May 2018
                Article
                1805.04612
                c77f6944-b166-4778-8dea-71a84a4a9a78

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Presented at IEEE International Conference on Acoustics, Speech and Signal Processing, 2018
                cs.SI cs.LG

                Social & Information networks,Artificial intelligence
                Social & Information networks, Artificial intelligence

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