Bộ nhớ dài-ngắn hạn (Vietnamese Wikipedia)

Analysis of information sources in references of the Wikipedia article "Bộ nhớ dài-ngắn hạn" in Vietnamese language version.

refsWebsite

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17doi.org

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13nih.gov

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7arxiv.org

69^th place

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6psu.edu

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3web.archive.org

1^st place

2acm.org

1,185^th place

930^th place

1stanford.edu

179^th place

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1researchgate.net

120^th place

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1idsia.ch

low place

9,721^st place

1googleusercontent.com

243^rd place

1,672^nd place

1academia.edu

121^st place

233^rd place

1jmlr.org

low place

1archive.org

6^th place

4^th place

1semanticscholar.org

11^th place

76^th place

1ucl.ac.be

5,389^th place

low place

1mlr.press

low place

1harvard.edu

18^th place

24^th place

1qilin-zhang.github.io

low place

1worldcat.org

5^th place

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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: 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. tr. 220–229. ISBN 978-3540746935.
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. tr. 577–584. ISBN 9781605603520.

archive.org

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. CiteSeerX 10.1.1.381.1992. doi:10.1016/s0893-6080(02)00219-8. PMID 12628609.

arxiv.org

Li, Xiangang; Wu, Xihong (ngày 15 tháng 10 năm 2014). "Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition". arΧiv:[1] [cs.CL].
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. arXiv:1303.5778. doi:10.1109/ICASSP.2013.6638947. ISBN 978-1-4799-0356-6.
Huang, Jie; Zhou, Wengang; Zhang, Qilin; Li, Houqiang; Li, Weiping (ngày 30 tháng 1 năm 2018). "Video-based Sign Language Recognition without Temporal Segmentation". arΧiv:1801.10111 [cs.CV].
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. tr. 477–492. arXiv:1612.02130. doi:10.1007/978-3-319-59536-8_30. ISBN 978-3-319-59535-1.
Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). “Doctor AI: Predicting Clinical Events via Recurrent Neural Networks”. Proceedings of the 1st Machine Learning for Healthcare Conference. 56: 301–318. arXiv:1511.05942. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600.
Jia, Robin; Liang, Percy (2016-06-11). Arxiv:1606.03622. arXiv:1606.03622 [cs].
Orsini, F.; Gastaldi, M.; Mantecchini, L.; Rossi, R. (2019). Neural networks trained with WiFi traces to predict airport passenger behavior. 6th International Conference on Models and Technologies for Intelligent Transportation Systems. Krakow: IEEE. arXiv:1910.14026. doi:10.1109/MTITS.2019.8883365. 8883365.

doi.org

Sepp Hochreiter; Jürgen Schmidhuber (1997). “Long short-term memory”. Neural Computation. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
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): 855–868. CiteSeerX 10.1.1.139.4502. doi:10.1109/tpami.2008.137. PMID 19299860.
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (tháng 10 năm 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. tr. 543–548. CiteSeerX 10.1.1.218.3399. doi:10.1109/IROS.2006.282190. ISBN 978-1-4244-0258-8.
Graves, A.; Schmidhuber, J. (2005). “Framewise phoneme classification with bidirectional LSTM and other neural network architectures”. Neural Networks. 18 (5–6): 602–610. CiteSeerX 10.1.1.331.5800. doi:10.1016/j.neunet.2005.06.042. PMID 16112549.
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. arXiv:1303.5778. doi:10.1109/ICASSP.2013.6638947. ISBN 978-1-4799-0356-6.
Eck, Douglas; Schmidhuber, Jürgen (ngày 28 tháng 8 năm 2002). Learning the Long-Term Structure of the Blues. Artificial Neural Networks — ICANN 2002. Lecture Notes in Computer Science. 2415. Springer, Berlin, Heidelberg. tr. 284–289. CiteSeerX 10.1.1.116.3620. doi:10.1007/3-540-46084-5_47. ISBN 978-3540460848.
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. CiteSeerX 10.1.1.11.7369. doi:10.1162/089976602320263980. PMID 12184841.
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. PMID 18249962. Bản gốc (PDF) lưu trữ ngày 10 tháng 7 năm 2020. Truy cập ngày 21 tháng 11 năm 2020.
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. CiteSeerX 10.1.1.381.1992. doi:10.1016/s0893-6080(02)00219-8. PMID 12628609.
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. 4 (3): 441–446. doi:10.1109/tcbb.2007.1015. PMID 17666763.
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. tr. 477–492. arXiv:1612.02130. doi:10.1007/978-3-319-59536-8_30. ISBN 978-3-319-59535-1.
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (ngày 22 tháng 5 năm 2018). “Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation” (PDF). Sensors. 18 (5): 1657. doi:10.3390/s18051657. ISSN 1424-8220. PMC 5982167. PMID 29789447.
Duan, Xuhuan; Wang, Le; Zhai, Changbo; Zheng, Nanning; Zhang, Qilin; Niu, Zhenxing; Hua, Gang (2018). Joint Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation. 25th IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2018.8451692. ISBN 978-1-4799-7061-2.
Orsini, F.; Gastaldi, M.; Mantecchini, L.; Rossi, R. (2019). Neural networks trained with WiFi traces to predict airport passenger behavior. 6th International Conference on Models and Technologies for Intelligent Transportation Systems. Krakow: IEEE. arXiv:1910.14026. doi:10.1109/MTITS.2019.8883365. 8883365.
Zhao, Z.; Chen, W.; Wu, X.; Chen, P.C.Y.; Liu, J. (2017). “LSTM network: A deep learning approach for Short-term traffic forecast”. IET Intelligent Transport Systems. 11 (2): 68–75. doi:10.1049/iet-its.2016.0208.
Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018). “Generative Recurrent Networks for De Novo Drug Design”. Mol Inform. 37 (1–2). doi:10.1002/minf.201700111. PMC 5836943. PMID 29095571.Quản lý CS1: nhiều tên: danh sách tác giả (liên kết)

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). Bản gốc (PDF) lưu trữ ngày 24 tháng 4 năm 2018.

harvard.edu

ui.adsabs.harvard.edu

Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). “Doctor AI: Predicting Clinical Events via Recurrent Neural Networks”. Proceedings of the 1st Machine Learning for Healthcare Conference. 56: 301–318. arXiv:1511.05942. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600.

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): 855–868. CiteSeerX 10.1.1.139.4502. doi:10.1109/tpami.2008.137. PMID 19299860.

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.

mlr.press

proceedings.mlr.press

Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). “Doctor AI: Predicting Clinical Events via Recurrent Neural Networks”. Proceedings of the 1st Machine Learning for Healthcare Conference. 56: 301–318. arXiv:1511.05942. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600.

nih.gov

pubmed.ncbi.nlm.nih.gov

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): 855–868. CiteSeerX 10.1.1.139.4502. doi:10.1109/tpami.2008.137. PMID 19299860.
Graves, A.; Schmidhuber, J. (2005). “Framewise phoneme classification with bidirectional LSTM and other neural network architectures”. Neural Networks. 18 (5–6): 602–610. CiteSeerX 10.1.1.331.5800. doi:10.1016/j.neunet.2005.06.042. PMID 16112549.
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. CiteSeerX 10.1.1.11.7369. doi:10.1162/089976602320263980. PMID 12184841.
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. PMID 18249962. Bản gốc (PDF) lưu trữ ngày 10 tháng 7 năm 2020. Truy cập ngày 21 tháng 11 năm 2020.
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. CiteSeerX 10.1.1.381.1992. doi:10.1016/s0893-6080(02)00219-8. PMID 12628609.
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. 4 (3): 441–446. doi:10.1109/tcbb.2007.1015. PMID 17666763.
Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). “Doctor AI: Predicting Clinical Events via Recurrent Neural Networks”. Proceedings of the 1st Machine Learning for Healthcare Conference. 56: 301–318. arXiv:1511.05942. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600.
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (ngày 22 tháng 5 năm 2018). “Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation” (PDF). Sensors. 18 (5): 1657. doi:10.3390/s18051657. ISSN 1424-8220. PMC 5982167. PMID 29789447.
Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018). “Generative Recurrent Networks for De Novo Drug Design”. Mol Inform. 37 (1–2). doi:10.1002/minf.201700111. PMC 5836943. PMID 29095571.Quản lý CS1: nhiều tên: danh sách tác giả (liên kết)

ncbi.nlm.nih.gov

Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). “Doctor AI: Predicting Clinical Events via Recurrent Neural Networks”. Proceedings of the 1st Machine Learning for Healthcare Conference. 56: 301–318. arXiv:1511.05942. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600.
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (ngày 22 tháng 5 năm 2018). “Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation” (PDF). Sensors. 18 (5): 1657. doi:10.3390/s18051657. ISSN 1424-8220. PMC 5982167. PMID 29789447.
Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018). “Generative Recurrent Networks for De Novo Drug Design”. Mol Inform. 37 (1–2). doi:10.1002/minf.201700111. PMC 5836943. PMID 29095571.Quản lý CS1: nhiều tên: danh sách tác giả (liên kết)

psu.edu

citeseerx.ist.psu.edu

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): 855–868. CiteSeerX 10.1.1.139.4502. doi:10.1109/tpami.2008.137. PMID 19299860.
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (tháng 10 năm 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. tr. 543–548. CiteSeerX 10.1.1.218.3399. doi:10.1109/IROS.2006.282190. ISBN 978-1-4244-0258-8.
Graves, A.; Schmidhuber, J. (2005). “Framewise phoneme classification with bidirectional LSTM and other neural network architectures”. Neural Networks. 18 (5–6): 602–610. CiteSeerX 10.1.1.331.5800. doi:10.1016/j.neunet.2005.06.042. PMID 16112549.
Eck, Douglas; Schmidhuber, Jürgen (ngày 28 tháng 8 năm 2002). Learning the Long-Term Structure of the Blues. Artificial Neural Networks — ICANN 2002. Lecture Notes in Computer Science. 2415. Springer, Berlin, Heidelberg. tr. 284–289. CiteSeerX 10.1.1.116.3620. doi:10.1007/3-540-46084-5_47. ISBN 978-3540460848.
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. CiteSeerX 10.1.1.11.7369. doi:10.1162/089976602320263980. PMID 12184841.
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. CiteSeerX 10.1.1.381.1992. doi:10.1016/s0893-6080(02)00219-8. PMID 12628609.

qilin-zhang.github.io

Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (ngày 22 tháng 5 năm 2018). “Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation” (PDF). Sensors. 18 (5): 1657. doi:10.3390/s18051657. ISSN 1424-8220. PMC 5982167. PMID 29789447.

researchgate.net

Sepp Hochreiter; Jürgen Schmidhuber (1997). “Long short-term memory”. Neural Computation. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.

semanticscholar.org

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. 4 (3): 441–446. doi:10.1109/tcbb.2007.1015. PMID 17666763.

stanford.edu

Mạng neural hồi quy cheatsheet, Standford University.

ucl.ac.be

elen.ucl.ac.be

Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Agarwal, Puneet (tháng 4 năm 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. Bản gốc (PDF) lưu trữ ngày 30 tháng 10 năm 2020. Truy cập ngày 21 tháng 11 năm 2020.

web.archive.org

Sak, Hasim; Senior, Andrew; Beaufays, Francoise (2014). “Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling” (PDF). Bản gốc (PDF) lưu trữ ngày 24 tháng 4 năm 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. PMID 18249962. Bản gốc (PDF) lưu trữ ngày 10 tháng 7 năm 2020. Truy cập ngày 21 tháng 11 năm 2020.
Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Agarwal, Puneet (tháng 4 năm 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. Bản gốc (PDF) lưu trữ ngày 30 tháng 10 năm 2020. Truy cập ngày 21 tháng 11 năm 2020.

worldcat.org

Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (ngày 22 tháng 5 năm 2018). “Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation” (PDF). Sensors. 18 (5): 1657. doi:10.3390/s18051657. ISSN 1424-8220. PMC 5982167. PMID 29789447.