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      You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

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      Computers, Environment and Urban Systems
      Elsevier BV

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Support-vector networks

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              Deep residual learning for image recognition

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                Author and article information

                Journal
                Computers, Environment and Urban Systems
                Computers, Environment and Urban Systems
                Elsevier BV
                01989715
                September 2020
                September 2020
                : 83
                : 101517
                Article
                10.1016/j.compenvurbsys.2020.101517
                f9222bca-0dc9-434e-af15-aba429e19343
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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