%0 Generic %1 salehinejad2017recent %A Salehinejad, Hojjat %A Sankar, Sharan %A Barfett, Joseph %A Colak, Errol %A Valaee, Shahrokh %D 2017 %K RNN seminar %T Recent Advances in Recurrent Neural Networks %U http://arxiv.org/abs/1801.01078 %X Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.

@misc{salehinejad2017recent, abstract = {Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.}, added-at = {2018-02-26T11:14:33.000+0100}, author = {Salehinejad, Hojjat and Sankar, Sharan and Barfett, Joseph and Colak, Errol and Valaee, Shahrokh}, biburl = {https://www.bibsonomy.org/bibtex/2185eb3aee251610748fa588e3f068279/jk_itwm}, description = {Recent Advances in Recurrent Neural Networks}, interhash = {41ca950dd8759914915fee770a6484d8}, intrahash = {185eb3aee251610748fa588e3f068279}, keywords = {RNN seminar}, note = {cite arxiv:1801.01078Comment: arXiv admin note: text overlap with arXiv:1602.04335}, timestamp = {2018-02-26T11:14:33.000+0100}, title = {Recent Advances in Recurrent Neural Networks}, url = {http://arxiv.org/abs/1801.01078}, year = 2017 }