Gated recurrent unit (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Gated recurrent unit" in English language version.

refsWebsite

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6arxiv.org

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5doi.org

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3semanticscholar.org

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2ieee.org

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2nih.gov

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1web.archive.org

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1wildml.com

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1harvard.edu

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arxiv.org (Global: 69^th place; English: 59^th place)

Ravanelli, Mirco; Brakel, Philemon; Omologo, Maurizio; Bengio, Yoshua (2018). "Light Gated Recurrent Units for Speech Recognition". IEEE Transactions on Emerging Topics in Computational Intelligence. 2 (2): 92–102. arXiv:1803.10225. Bibcode:2018ITECI...2...92R. doi:10.1109/TETCI.2017.2762739. S2CID 4402991.
Su, Yuahang; Kuo, Jay (2019). "On extended long short-term memory and dependent bidirectional recurrent neural network". Neurocomputing. 356: 151–161. arXiv:1803.01686. doi:10.1016/j.neucom.2019.04.044. S2CID 3675055.
Chung, Junyoung; Gulcehre, Caglar; Cho, KyungHyun; Bengio, Yoshua (2014). "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling". arXiv:1412.3555 [cs.NE].
Chung, Junyoung; Gulcehre, Caglar; Cho, KyungHyun; Bengio, Yoshua (2014). "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling". arXiv:1412.3555 [cs.NE].
Dey, Rahul; Salem, Fathi M. (2017-01-20). "Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks". arXiv:1701.05923 [cs.NE].
Heck, Joel; Salem, Fathi M. (2017-01-12). "Simplified Minimal Gated Unit Variations for Recurrent Neural Networks". arXiv:1701.03452 [cs.NE].

doi.org (Global: 2^nd place; English: 2^nd place)

Cho, Kyunghyun; van Merrienboer, Bart; Bahdanau, Dzmitry; Bengio, Yoshua (2014). "On the Properties of Neural Machine Translation: Encoder–Decoder Approaches". Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation: 103–111. doi:10.3115/v1/W14-4012.
Felix Gers; Jürgen Schmidhuber; Fred Cummins (1999). "Learning to forget: Continual prediction with LSTM". 9th International Conference on Artificial Neural Networks: ICANN '99. Vol. 1999. pp. 850–855. doi:10.1049/cp:19991218. ISBN 0-85296-721-7.
Ravanelli, Mirco; Brakel, Philemon; Omologo, Maurizio; Bengio, Yoshua (2018). "Light Gated Recurrent Units for Speech Recognition". IEEE Transactions on Emerging Topics in Computational Intelligence. 2 (2): 92–102. arXiv:1803.10225. Bibcode:2018ITECI...2...92R. doi:10.1109/TETCI.2017.2762739. S2CID 4402991.
Su, Yuahang; Kuo, Jay (2019). "On extended long short-term memory and dependent bidirectional recurrent neural network". Neurocomputing. 356: 151–161. arXiv:1803.01686. doi:10.1016/j.neucom.2019.04.044. S2CID 3675055.
Gruber, N.; Jockisch, A. (2020), "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?", Frontiers in Artificial Intelligence, 3 40, doi:10.3389/frai.2020.00040, PMC 7861254, PMID 33733157, S2CID 220252321

harvard.edu (Global: 18^th place; English: 17^th place)

ui.adsabs.harvard.edu

Ravanelli, Mirco; Brakel, Philemon; Omologo, Maurizio; Bengio, Yoshua (2018). "Light Gated Recurrent Units for Speech Recognition". IEEE Transactions on Emerging Topics in Computational Intelligence. 2 (2): 92–102. arXiv:1803.10225. Bibcode:2018ITECI...2...92R. doi:10.1109/TETCI.2017.2762739. S2CID 4402991.

ieee.org (Global: 652^nd place; English: 515^th place)

ieeexplore.ieee.org

Felix Gers; Jürgen Schmidhuber; Fred Cummins (1999). "Learning to forget: Continual prediction with LSTM". 9th International Conference on Artificial Neural Networks: ICANN '99. Vol. 1999. pp. 850–855. doi:10.1049/cp:19991218. ISBN 0-85296-721-7.
Bittar, Alexandre; Garner, Philip N. (May 2021). "A Bayesian Interpretation of the Light Gated Recurrent Unit". ICASSP 2021. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, ON, Canada: IEEE. pp. 2965–2969. 10.1109/ICASSP39728.2021.9414259.

nih.gov (Global: 4^th place; English: 4^th place)

ncbi.nlm.nih.gov

Gruber, N.; Jockisch, A. (2020), "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?", Frontiers in Artificial Intelligence, 3 40, doi:10.3389/frai.2020.00040, PMC 7861254, PMID 33733157, S2CID 220252321

pubmed.ncbi.nlm.nih.gov

Gruber, N.; Jockisch, A. (2020), "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?", Frontiers in Artificial Intelligence, 3 40, doi:10.3389/frai.2020.00040, PMC 7861254, PMID 33733157, S2CID 220252321

semanticscholar.org (Global: 11^th place; English: 8^th place)

api.semanticscholar.org

Ravanelli, Mirco; Brakel, Philemon; Omologo, Maurizio; Bengio, Yoshua (2018). "Light Gated Recurrent Units for Speech Recognition". IEEE Transactions on Emerging Topics in Computational Intelligence. 2 (2): 92–102. arXiv:1803.10225. Bibcode:2018ITECI...2...92R. doi:10.1109/TETCI.2017.2762739. S2CID 4402991.
Su, Yuahang; Kuo, Jay (2019). "On extended long short-term memory and dependent bidirectional recurrent neural network". Neurocomputing. 356: 151–161. arXiv:1803.01686. doi:10.1016/j.neucom.2019.04.044. S2CID 3675055.
Gruber, N.; Jockisch, A. (2020), "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?", Frontiers in Artificial Intelligence, 3 40, doi:10.3389/frai.2020.00040, PMC 7861254, PMID 33733157, S2CID 220252321

web.archive.org (Global: 1^st place; English: 1^st place)

"Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano – WildML". Wildml.com. 2015-10-27. Archived from the original on 2021-11-10. Retrieved May 18, 2016.

wildml.com (Global: low place; English: low place)

"Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano – WildML". Wildml.com. 2015-10-27. Archived from the original on 2021-11-10. Retrieved May 18, 2016.

Gated recurrent unit (English Wikipedia)

arxiv.org (Global: 69th place; English: 59th place)

doi.org (Global: 2nd place; English: 2nd place)

harvard.edu (Global: 18th place; English: 17th place)