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      Deep learning for visual understanding: A review

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      Neurocomputing

      Elsevier BV

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          Deep Learning in Neural Networks: An Overview

            (2014)
          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Extracting and composing robust features with denoising autoencoders

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              Sparse coding with an overcomplete basis set: A strategy employed by V1?

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                Author and article information

                Journal
                Neurocomputing
                Neurocomputing
                Elsevier BV
                09252312
                April 2016
                April 2016
                : 187
                :
                : 27-48
                Article
                10.1016/j.neucom.2015.09.116
                © 2016

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