Rekurrentne närvivõrk (Estonian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Rekurrentne närvivõrk" in Estonian language version.

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14doi.org

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3acm.org

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2googleusercontent.com

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2nips.cc

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2harvard.edu

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2nih.gov

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2archive.org

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2springer.com

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1wikipedia.org

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1academia.edu

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1ucl.ac.be

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academia.edu

Wierstra, Daan; Schmidhuber, J.; Gomez, F. J. (2005). "Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning". Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh (inglise): 853–858.

acm.org

dl.acm.org

Fernández, Santiago; Graves, Alex; Schmidhuber, Jürgen (2007). "An Application of Recurrent Neural Networks to Discriminative Keyword Spotting". Proceedings of the 17th International Conference on Artificial Neural Networks. ICANN'07. Berlin, Heidelberg: Springer-Verlag: 220–229. ISBN 978-3-540-74693-5.
Fernández, Santiago; Graves, Alex; Schmidhuber, Jürgen (2007). "An Application of Recurrent Neural Networks to Discriminative Keyword Spotting". Proceedings of the 17th International Conference on Artificial Neural Networks. ICANN'07. Berlin, Heidelberg: Springer-Verlag: 220–229. ISBN 3540746935.
Graves, Alex; Fernández, Santiago; Liwicki, Marcus; Bunke, Horst; Schmidhuber, Jürgen (2007). "Unconstrained Online Handwriting Recognition with Recurrent Neural Networks". Proceedings of the 20th International Conference on Neural Information Processing Systems. NIPS'07. USA: Curran Associates Inc.: 577–584. ISBN 9781605603520.

archive.org

Graves, Alex; Mohamed, Abdel-rahman; Hinton, Geoffrey (2013). "Speech Recognition with Deep Recurrent Neural Networks". Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on: 6645–6649.
Perez-Ortiz, J. A.; Gers, F. A.; Eck, D.; Schmidhuber, J. (2003). "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets". Neural Networks. 16 (2): 241–250. DOI:10.1016/s0893-6080(02)00219-8.

arxiv.org

Sutskever, L.; Vinyals, O.; Le, Q. (2014). "Sequence to Sequence Learning with Neural Networks" (PDF). Electronic Proceedings of the Neural Information Processing Systems Conference. 27: 5346. arXiv:1409.3215. Bibcode:2014arXiv1409.3215S.
Hojjat, Salehinejad; Sankar, Sharan; Barfett, Joseph (2014). "Recent Advances in Recurrent Neural Networks" (PDF): 3. arXiv:1801.01078. Bibcode:201. Vaadatud 29.10.2018. {{cite journal}}: viitemall journal nõuab parameetrit |journal= (juhend); kontrolli parameetri |bibcode= pikkust (juhend)
Schmidhuber, Jürgen (jaanuar 2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828. DOI:10.1016/j.neunet.2014.09.003. PMID 25462637.
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). "Predictive Business Process Monitoring with LSTM neural networks". Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE). Lecture Notes in Computer Science. 10253: 477–492. arXiv:1612.02130. DOI:10.1007/978-3-319-59536-8_30. ISBN 978-3-319-59535-1.

doi.org

Graves, A.; Liwicki, M.; Fernandez, S.; Bertolami, R.; Bunke, H.; Schmidhuber, J. (2009). "A Novel Connectionist System for Improved Unconstrained Handwriting Recognition" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5). DOI:10.1109/tpami.2008.137.
Hochreiter, Sepp; Schmidhuber, Jürgen (1. november 1997). "Long Short-Term Memory". Neural Computation. 9 (8): 1735–1780. DOI:10.1162/neco.1997.9.8.1735.
Schmidhuber, Jürgen (jaanuar 2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828. DOI:10.1016/j.neunet.2014.09.003. PMID 25462637.
Bayer, Justin; Wierstra, Daan; Togelius, Julian; Schmidhuber, Jürgen (14. september 2009). "Evolving Memory Cell Structures for Sequence Learning". Artificial Neural Networks – ICANN 2009. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 5769: 755–764. DOI:10.1007/978-3-642-04277-5_76. ISBN 978-3-642-04276-8.
Gers, F. A.; Schmidhuber, E. (november 2001). "LSTM recurrent networks learn simple context-free and context-sensitive languages". IEEE Transactions on Neural Networks. 12 (6): 1333–1340. DOI:10.1109/72.963769. ISSN 1045-9227.
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (oktoober 2006). "A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks". 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems: 543–548. DOI:10.1109/IROS.2006.282190. ISBN 1-4244-0258-1.
Graves, A.; Schmidhuber, J. (2005). "Framewise phoneme classification with bidirectional LSTM and other neural network architectures". Neural Networks. 18 (5–6): 602–610. DOI:10.1016/j.neunet.2005.06.042.
Eck, Douglas; Schmidhuber, Jürgen (28. august 2002). "Learning the Long-Term Structure of the Blues". Artificial Neural Networks — ICANN 2002. Lecture Notes in Computer Science (inglise). Springer, Berlin, Heidelberg. 2415: 284–289. DOI:10.1007/3-540-46084-5_47. ISBN 3540460845.
Schmidhuber, J.; Gers, F.; Eck, D.; Schmidhuber, J.; Gers, F. (2002). "Learning nonregular languages: A comparison of simple recurrent networks and LSTM". Neural Computation. 14 (9): 2039–2041. DOI:10.1162/089976602320263980.
Gers, F. A.; Schmidhuber, J. (2001). "LSTM Recurrent Networks Learn Simple Context Free and Context Sensitive Languages" (PDF). IEEE Transactions on Neural Networks. 12 (6): 1333–1340. DOI:10.1109/72.963769. Originaali (PDF) arhiivikoopia seisuga 10. juuli 2020. Vaadatud 3. detsembril 2018.
Perez-Ortiz, J. A.; Gers, F. A.; Eck, D.; Schmidhuber, J. (2003). "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets". Neural Networks. 16 (2): 241–250. DOI:10.1016/s0893-6080(02)00219-8.
Hochreiter, S.; Heusel, M.; Obermayer, K. (2007). "Fast model-based protein homology detection without alignment". Bioinformatics. 23 (14): 1728–1736. DOI:10.1093/bioinformatics/btm247. PMID 17488755.
Thireou, T.; Reczko, M. (2007). "Bidirectional Long Short-Term Memory Networks for predicting the subcellular localization of eukaryotic proteins". IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 4 (3): 441–446. DOI:10.1109/tcbb.2007.1015.
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). "Predictive Business Process Monitoring with LSTM neural networks". Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE). Lecture Notes in Computer Science. 10253: 477–492. arXiv:1612.02130. DOI:10.1007/978-3-319-59536-8_30. ISBN 978-3-319-59535-1.

googleresearch.blogspot.ch

Sak, Haşim; Senior, Andrew; Rao, Kanishka; Beaufays, Françoise; Schalkwyk, Johan (september 2015). "Google voice search: faster and more accurate". Vaadatud 30.10.2018.

googleusercontent.com

static.googleusercontent.com

Sak, Hasim; Senior, Andrew; Beaufays, Francoise (2014). "Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling" (PDF). Vaadatud 29.10.2018.
Zen, Heiga; Sak, Hasim (2015). "Unidirectional Long Short-Term Memory Recurrent Neural Network with Recurrent Output Layer for Low-Latency Speech Synthesis" (PDF). Google.com. ICASSP. Lk 4470–4474. Vaadatud 29.10.2018.

harvard.edu

ui.adsabs.harvard.edu

Sutskever, L.; Vinyals, O.; Le, Q. (2014). "Sequence to Sequence Learning with Neural Networks" (PDF). Electronic Proceedings of the Neural Information Processing Systems Conference. 27: 5346. arXiv:1409.3215. Bibcode:2014arXiv1409.3215S.
Hojjat, Salehinejad; Sankar, Sharan; Barfett, Joseph (2014). "Recent Advances in Recurrent Neural Networks" (PDF): 3. arXiv:1801.01078. Bibcode:201. Vaadatud 29.10.2018. {{cite journal}}: viitemall journal nõuab parameetrit |journal= (juhend); kontrolli parameetri |bibcode= pikkust (juhend)

idsia.ch

Graves, A.; Liwicki, M.; Fernandez, S.; Bertolami, R.; Bunke, H.; Schmidhuber, J. (2009). "A Novel Connectionist System for Improved Unconstrained Handwriting Recognition" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5). DOI:10.1109/tpami.2008.137.

ieee.org

ieeexplore.ieee.org

Gers, F. A.; Schmidhuber, E. (november 2001). "LSTM recurrent networks learn simple context-free and context-sensitive languages". IEEE Transactions on Neural Networks. 12 (6): 1333–1340. DOI:10.1109/72.963769. ISSN 1045-9227.
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (oktoober 2006). "A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks". 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems: 543–548. DOI:10.1109/IROS.2006.282190. ISBN 1-4244-0258-1.

ijcse.com

Miljanovic, Milos (veebruar 2012). "Comparative analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction" (PDF). Indian Journal of Computer and Engineering. 3 (1).CS1 maint: Date format (link)

jmlr.org

Gers, F.; Schraudolph, N.; Schmidhuber, J. (2002). "Learning precise timing with LSTM recurrent networks" (PDF). Journal of Machine Learning Research. 3: 115–143.

nih.gov

pubmed.ncbi.nlm.nih.gov

Schmidhuber, Jürgen (jaanuar 2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828. DOI:10.1016/j.neunet.2014.09.003. PMID 25462637.
Hochreiter, S.; Heusel, M.; Obermayer, K. (2007). "Fast model-based protein homology detection without alignment". Bioinformatics. 23 (14): 1728–1736. DOI:10.1093/bioinformatics/btm247. PMID 17488755.

nips.cc

papers.nips.cc

Graves, Alex; Schmidhuber, Jürgen (2009). Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris editor-K. I.; Culotta, Aron (toim-d). "Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks". Neural Information Processing Systems (NIPS) Foundation: 545–552. {{cite journal}}: parameetris |editor-first4= on üldnimi (juhend)
Sutskever, L.; Vinyals, O.; Le, Q. (2014). "Sequence to Sequence Learning with Neural Networks" (PDF). Electronic Proceedings of the Neural Information Processing Systems Conference. 27: 5346. arXiv:1409.3215. Bibcode:2014arXiv1409.3215S.

researchgate.net

Gers, Felix; Schraudolph, Nicol N.; Schmidhuber, Jürgen. "Learning Precise Timing with LSTM Recurrent Networks (PDF Download Available)". ResearchGate. Lk 115–143. Vaadatud 29.10.2018.

springer.com

link.springer.com

Eck, Douglas; Schmidhuber, Jürgen (28. august 2002). "Learning the Long-Term Structure of the Blues". Artificial Neural Networks — ICANN 2002. Lecture Notes in Computer Science (inglise). Springer, Berlin, Heidelberg. 2415: 284–289. DOI:10.1007/3-540-46084-5_47. ISBN 3540460845.
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). "Predictive Business Process Monitoring with LSTM neural networks". Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE). Lecture Notes in Computer Science. 10253: 477–492. arXiv:1612.02130. DOI:10.1007/978-3-319-59536-8_30. ISBN 978-3-319-59535-1.

ucl.ac.be

elen.ucl.ac.be

Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Agarwal, Puneet (aprill 2015). "Long Short Term Memory Networks for Anomaly Detection in Time Series" (PDF). European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — ESANN 2015. Originaali (PDF) arhiivikoopia seisuga 30. oktoober 2020. Vaadatud 3. detsembril 2018.

web.archive.org

Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Agarwal, Puneet (aprill 2015). "Long Short Term Memory Networks for Anomaly Detection in Time Series" (PDF). European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — ESANN 2015. Originaali (PDF) arhiivikoopia seisuga 30. oktoober 2020. Vaadatud 3. detsembril 2018.
Gers, F. A.; Schmidhuber, J. (2001). "LSTM Recurrent Networks Learn Simple Context Free and Context Sensitive Languages" (PDF). IEEE Transactions on Neural Networks. 12 (6): 1333–1340. DOI:10.1109/72.963769. Originaali (PDF) arhiivikoopia seisuga 10. juuli 2020. Vaadatud 3. detsembril 2018.

wikipedia.org

en.wikipedia.org

Miljanovic, Milos (veebruar 2012). "Comparative analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction" (PDF). Indian Journal of Computer and Engineering. 3 (1).CS1 maint: Date format (link)

wired.com

Cade Metz (16.05.2016). "Google Built Its Very Own Chips to Power Its AI Bots". Wired.

worldcat.org

Gers, F. A.; Schmidhuber, E. (november 2001). "LSTM recurrent networks learn simple context-free and context-sensitive languages". IEEE Transactions on Neural Networks. 12 (6): 1333–1340. DOI:10.1109/72.963769. ISSN 1045-9227.