Long short-term memory (Portuguese Wikipedia)

Analysis of information sources in references of the Wikipedia article "Long short-term memory" in Portuguese language version.

refsWebsite

Global rank Portuguese rank

27doi.org

2^nd place

4^th place

14arxiv.org

69^th place

195^th place

13nih.gov

4^th place

8^th place

10psu.edu

207^th place

589^th place

4worldcat.org

5^th place

4harvard.edu

18^th place

51^st place

3web.archive.org

1^st place

3idsia.ch

low place

3acm.org

1,185^th place

1,301^st place

2researchgate.net

120^th place

157^th place

2jku.at

low place

2googleresearch.blogspot.co.at

low place

2kdd.org

low place

1googleusercontent.com

243^rd place

261^st place

1theverge.com

114^th place

219^th place

1openai.com

1,559^th place

1,438^th place

1towardsdatascience.com

8,920^th place

6,719^th place

1medium.com

551^st place

330^th place

1neurips.cc

low place

1aclanthology.org

low place

1uva.nl

3,518^th place

3,782^nd place

1handle.net

102^nd place

459^th place

1jmlr.org

low place

1academia.edu

121^st place

131^st place

1ucl.ac.be

5,389^th place

8,741^st place

1mlr.press

low place

1qilin-zhang.github.io

low place

1wikidata.org

43^rd place

440^th place

1googleresearch.blogspot.co.uk

low place

1newatlas.com

5,636^th place

low place

1googleblog.blogspot.com

3,598^th place

2,195^th place

1nips.cc

low place

1ieee.org

652^nd place

920^th place

1wired.com

193^rd place

272^nd place

1googleblog.com

1,272^nd place

1,479^th place

1theinformation.com

low place

1zdnet.com

786^th place

1,412^th place

1apple.com

67^th place

97^th place

1bgr.com

7,049^th place

low place

1isca-speech.org

low place

1allthingsdistributed.com

low place

1msu.edu

1,844^th place

2,191^st place

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

aclanthology.org

Lakretz, Yair; Kruszewski, German; Desbordes, Theo; Hupkes, Dieuwke; Dehaene, Stanislas; Baroni, Marco (2019), «The emergence of number and syntax units in», The emergence of number and syntax units (PDF), Association for Computational Linguistics, pp. 11–20, doi:10.18653/v1/N19-1002, hdl:11245.1/16cb6800-e10d-4166-8e0b-fed61ca6ebb4

acm.org

dl.acm.org

Hochreiter, Sepp; Schmidhuber, Juergen (1996). LSTM can solve hard long time lag problems. Advances in Neural Information Processing Systems
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. Col: ICANN'07. Berlin, Heidelberg: Springer-Verlag. pp. 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. Col: NIPS'07. USA: Curran Associates Inc. pp. 577–584. ISBN 9781605603520

allthingsdistributed.com

Vogels, Werner (30 de novembro de 2016). «Bringing the Magic of Amazon AI and Alexa to Apps on AWS. – All Things Distributed». www.allthingsdistributed.com. Consultado em 27 de junho de 2017

apple.com

machinelearning.apple.com

«Can Global Semantic Context Improve Neural Language Models? – Apple». Apple Machine Learning Journal (em inglês). Consultado em 30 de abril de 2020

arxiv.org

Li, Xiangang; Wu, Xihong (15 de outubro de 2014). «Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition». arXiv:1410.4281 [cs.CL]
Wu, Yonghui; Schuster, Mike; Chen, Zhifeng; Le, Quoc V.; Norouzi, Mohammad; Macherey, Wolfgang; Krikun, Maxim; Cao, Yuan; Gao, Qin (26 de setembro de 2016). «Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation». arXiv:1609.08144 [cs.CL]
Sahidullah, Md; Patino, Jose; Cornell, Samuele; Yin, Ruiking; Sivasankaran, Sunit; Bredin, Herve; Korshunov, Pavel; Brutti, Alessio; Serizel, Romain; Vincent, Emmanuel; Evans, Nicholas; Marcel, Sebastien; Squartini, Stefano; Barras, Claude (6 de novembro de 2019). «The Speed Submission to DIHARD II: Contributions & Lessons Learned». arXiv:1911.02388 [eess.AS]
Xingjian Shi; Zhourong Chen; Hao Wang; Dit-Yan Yeung; Wai-kin Wong; Wang-chun Woo (2015). «Rede LSTM Convolucional: Uma Abordagem de Aprendizado de Máquina para Previsão Imediata de Precipitação». Atas da 28ª Conferência Internacional sobre Sistemas de Informação Neural: 802–810. Bibcode:2015arXiv150604214S. arXiv:1506.04214
Graves, Alex; Mohamed, Abdel-rahman; Hinton, Geoffrey (2013). «Speech recognition with deep recurrent neural networks». 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. [S.l.: s.n.] pp. 6645–6649. ISBN 978-1-4799-0356-6. arXiv:1303.5778. doi:10.1109/ICASSP.2013.6638947
Huang, Jie; Zhou, Wengang; Zhang, Qilin; Li, Houqiang; Li, Weiping (30 de janeiro de 2018). «Video-based Sign Language Recognition without Temporal Segmentation». arXiv:1801.10111 [cs.CV]
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). «Predictive Business Process Monitoring with LSTM Neural Networks». Advanced Information Systems Engineering. Col: Lecture Notes in Computer Science. 10253. [S.l.: s.n.] pp. 477–492. ISBN 978-3-319-59535-1. arXiv:1612.02130. doi:10.1007/978-3-319-59536-8_30
Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). «Doctor AI: Predicting Clinical Events via Recurrent Neural Networks». JMLR Workshop and Conference Proceedings. 56: 301–318. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600. arXiv:1511.05942
Jia, Robin; Liang, Percy (2016). «Data Recombination for Neural Semantic Parsing». arXiv:1606.03622 [cs.CL]
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
Klaus Greff; Rupesh Kumar Srivastava; Jan Koutník; Bas R. Steunebrink; Jürgen Schmidhuber (2015). «LSTM: A Search Space Odyssey». IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. Bibcode:2015arXiv150304069G. PMID 27411231. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924
Schmidhuber, Juergen (10 de Maio de 2021). «Deep Learning: Our Miraculous Year 1990-1991». arXiv:2005.05744 [cs.NE]
Cho, Kyunghyun; van Merrienboer, Bart; Gulcehre, Caglar; Bahdanau, Dzmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (2014). «Aprendizado de Representações de Frases usando Codificador-Decodificador RNN para Tradução Estatística de Máquinas». arXiv:1406.1078 [cs.CL]
Srivastava, Rupesh Kumar; Greff, Klaus; Schmidhuber, Jürgen (2 de maio de 2015). «Redes Highway». arXiv:1505.00387 [cs.LG]

bgr.com

Smith, Chris (13 de junho de 2016). «iOS 10: Siri now works in third-party apps, comes with extra AI features». BGR. Consultado em 27 de junho de 2017

doi.org

dx.doi.org

Sepp Hochreiter; Jürgen Schmidhuber (1997). «Long short-term memory». Neural Computation. 9 (8): 1735–1780. PMID 9377276. doi:10.1162/neco.1997.9.8.1735
Graves, A.; Liwicki, M.; Fernández, S.; Bertolami, R.; Bunke, H.; Schmidhuber, J. (maio de 2009). «Um Novo Sistema Conexista para Reconhecimento de Escrita à Mão Sem Restrições». IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5): 855–868. CiteSeerX 10.1.1.139.4502. ISSN 0162-8828. PMID 19299860. doi:10.1109/tpami.2008.137
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (Outubro de 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. [S.l.: s.n.] pp. 543–548. CiteSeerX 10.1.1.218.3399. ISBN 978-1-4244-0258-8. doi:10.1109/IROS.2006.282190
Felix A. Gers; Jürgen Schmidhuber; Fred Cummins (2000). «Learning to Forget: Continual Prediction with LSTM». Neural Computation. 12 (10): 2451–2471. CiteSeerX 10.1.1.55.5709. PMID 11032042. doi:10.1162/089976600300015015
Lakretz, Yair; Kruszewski, German; Desbordes, Theo; Hupkes, Dieuwke; Dehaene, Stanislas; Baroni, Marco (2019), «The emergence of number and syntax units in», The emergence of number and syntax units (PDF), Association for Computational Linguistics, pp. 11–20, doi:10.18653/v1/N19-1002, hdl:11245.1/16cb6800-e10d-4166-8e0b-fed61ca6ebb4
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. PMID 18249962. doi:10.1109/72.963769
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. PMID 16112549. doi:10.1016/j.neunet.2005.06.042
Graves, Alex; Mohamed, Abdel-rahman; Hinton, Geoffrey (2013). «Speech recognition with deep recurrent neural networks». 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. [S.l.: s.n.] pp. 6645–6649. ISBN 978-1-4799-0356-6. arXiv:1303.5778. doi:10.1109/ICASSP.2013.6638947
Eck, Douglas; Schmidhuber, Jürgen (28 de agosto de 2002). «Learning the Long-Term Structure of the Blues». Artificial Neural Networks — ICANN 2002. Col: Lecture Notes in Computer Science. 2415. [S.l.]: Springer, Berlin, Heidelberg. pp. 284–289. CiteSeerX 10.1.1.116.3620. ISBN 978-3540460848. doi:10.1007/3-540-46084-5_47
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. PMID 12184841. doi:10.1162/089976602320263980
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. PMID 12628609. doi:10.1016/s0893-6080(02)00219-8
Baccouche, M.; Mamalet, F.; Wolf, C.; Garcia, C.; Baskurt, A. (2011). «Sequential Deep Learning for Human Action Recognition». In: Salah, A. A.; Lepri, B. 2nd International Workshop on Human Behavior Understanding (HBU). Col: Lecture Notes in Computer Science. 7065. Amsterdam, Netherlands: Springer. pp. 29–39. ISBN 978-3-642-25445-1. doi:10.1007/978-3-642-25446-8_4
Hochreiter, S.; Heusel, M.; Obermayer, K. (2007). «Fast model-based protein homology detection without alignment». Bioinformatics. 23 (14): 1728–1736. PMID 17488755. doi:10.1093/bioinformatics/btm247
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. PMID 17666763. doi:10.1109/tcbb.2007.1015
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). «Predictive Business Process Monitoring with LSTM Neural Networks». Advanced Information Systems Engineering. Col: Lecture Notes in Computer Science. 10253. [S.l.: s.n.] pp. 477–492. ISBN 978-3-319-59535-1. arXiv:1612.02130. doi:10.1007/978-3-319-59536-8_30
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (22 de maio de 2018). «Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation» (PDF). Sensors. 18 (5): 1657. Bibcode:2018Senso..18.1657W. ISSN 1424-8220. PMC 5982167. PMID 29789447. doi:10.3390/s18051657
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». 2018 25th IEEE International Conference on Image Processing (ICIP). 25th IEEE International Conference on Image Processing (ICIP). pp. 918–922. ISBN 978-1-4799-7061-2. doi:10.1109/icip.2018.8451692
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). PMC 5836943. PMID 29095571. doi:10.1002/minf.201700111
Saiful Islam, Md.; Hossain, Emam (26 de outubro de 2020). «Foreign Exchange Currency Rate Prediction using a GRU-LSTM Hybrid Network». Soft Computing Letters (em inglês). 3. 100009 páginas. ISSN 2666-2221. doi:10.1016/j.socl.2020.100009
Klaus Greff; Rupesh Kumar Srivastava; Jan Koutník; Bas R. Steunebrink; Jürgen Schmidhuber (2015). «LSTM: A Search Space Odyssey». IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. Bibcode:2015arXiv150304069G. PMID 27411231. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924
Gers, Felix; Schmidhuber, Jürgen; Cummins, Fred (1999). «Learning to forget: Continual prediction with LSTM». 9th International Conference on Artificial Neural Networks: ICANN '99. 1999. [S.l.: s.n.] pp. 850–855. ISBN 0-85296-721-7. doi:10.1049/cp:19991218
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). «Learning to Learn Using Gradient Descent». Artificial Neural Networks — ICANN 2001 (PDF). Col: Lecture Notes in Computer Science. 2130. [S.l.: s.n.] pp. 87–94. CiteSeerX 10.1.1.5.323. ISBN 978-3-540-42486-4. ISSN 0302-9743. doi:10.1007/3-540-44668-0_13
Märgner, Volker; Abed, Haikal El (julho de 2009). «Competição de Reconhecimento de Escrita Árabe ICDAR 2009». Conferência Internacional de Análise e Reconhecimento de Documentos de 2009. [S.l.: s.n.] pp. 1383–1387. ISBN 978-1-4244-4500-4. doi:10.1109/ICDAR.2009.256
He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian (2016). Aprendizado Profundo Residual para Reconhecimento de Imagens. Conferência IEEE sobre Visão Computacional e Reconhecimento de Padrões (CVPR) de 2016. Las Vegas, NV, EUA: IEEE. pp. 770–778. ISBN 978-1-4673-8851-1. doi:10.1109/CVPR.2016.90
Capes, Tim; Coles, Paul; Conkie, Alistair; Golipour, Ladan; Hadjitarkhani, Abie; Hu, Qiong; Huddleston, Nancy; Hunt, Melvyn; Li, Jiangchuan; Neeracher, Matthias; Prahallad, Kishore (20 de agosto de 2017). «Siri On-Device Deep Learning-Guided Unit Selection Text-to-Speech System». ISCA. Interspeech 2017 (em inglês): 4011–4015. doi:10.21437/Interspeech.2017-1798

googleblog.blogspot.com

«Prescrição de Neon... ou melhor, Nova transcrição para o Google Voice». Blog Oficial do Google (em inglês). 23 de julho de 2015

googleblog.com

ai.googleblog.com

«A Neural Network for Machine Translation, at Production Scale». Google AI Blog (em inglês). 27 de setembro de 2016. Consultado em 25 de abril de 2020

googleresearch.blogspot.co.at

Beaufays, Françoise (11 de agosto de 2015). «As redes neurais por trás da transcrição do Google Voice». Blog de Pesquisa
Khaitan, Pranav (18 de maio de 2016). «Chat Smarter with Allo». Research Blog. Consultado em 27 de junho de 2017

googleresearch.blogspot.co.uk

Sak, Haşim; Senior, Andrew; Rao, Kanishka; Beaufays, Françoise; Schalkwyk, Johan (24 de setembro de 2015). «Busca por voz do Google: mais rápida e precisa». Blog de Pesquisa (em inglês)

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). Cópia arquivada (PDF) em 24 de abril de 2018

handle.net

hdl.handle.net

Lakretz, Yair; Kruszewski, German; Desbordes, Theo; Hupkes, Dieuwke; Dehaene, Stanislas; Baroni, Marco (2019), «The emergence of number and syntax units in», The emergence of number and syntax units (PDF), Association for Computational Linguistics, pp. 11–20, doi:10.18653/v1/N19-1002, hdl:11245.1/16cb6800-e10d-4166-8e0b-fed61ca6ebb4

harvard.edu

ui.adsabs.harvard.edu

Xingjian Shi; Zhourong Chen; Hao Wang; Dit-Yan Yeung; Wai-kin Wong; Wang-chun Woo (2015). «Rede LSTM Convolucional: Uma Abordagem de Aprendizado de Máquina para Previsão Imediata de Precipitação». Atas da 28ª Conferência Internacional sobre Sistemas de Informação Neural: 802–810. Bibcode:2015arXiv150604214S. arXiv:1506.04214
Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). «Doctor AI: Predicting Clinical Events via Recurrent Neural Networks». JMLR Workshop and Conference Proceedings. 56: 301–318. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600. arXiv:1511.05942
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (22 de maio de 2018). «Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation» (PDF). Sensors. 18 (5): 1657. Bibcode:2018Senso..18.1657W. ISSN 1424-8220. PMC 5982167. PMID 29789447. doi:10.3390/s18051657
Klaus Greff; Rupesh Kumar Srivastava; Jan Koutník; Bas R. Steunebrink; Jürgen Schmidhuber (2015). «LSTM: A Search Space Odyssey». IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. Bibcode:2015arXiv150304069G. PMID 27411231. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924

idsia.ch

people.idsia.ch

Schmidhuber, Jürgen (2021). «A década de 2010: Nossa Década de Aprendizado Profundo / Perspectivas para a década de 2020». AI Blog. IDSIA, Suíça. Consultado em 30 de abril de 2022
Wierstra, Daan; Foerster, Alexander; Peters, Jan; Schmidhuber, Juergen (2005). «Solving Deep Memory POMDPs with Recurrent Policy Gradients». International Conference on Artificial Neural Networks ICANN'07
Schmidhuber, Jürgen (2021). «As redes neurais mais citadas todas se baseiam em trabalhos feitos nos meus laboratórios». Blog de IA. IDSIA, Suíça

ieee.org

ieeexplore.ieee.org

He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian (2016). Aprendizado Profundo Residual para Reconhecimento de Imagens. Conferência IEEE sobre Visão Computacional e Reconhecimento de Padrões (CVPR) de 2016. Las Vegas, NV, EUA: IEEE. pp. 770–778. ISBN 978-1-4673-8851-1. doi:10.1109/CVPR.2016.90

isca-speech.org

Capes, Tim; Coles, Paul; Conkie, Alistair; Golipour, Ladan; Hadjitarkhani, Abie; Hu, Qiong; Huddleston, Nancy; Hunt, Melvyn; Li, Jiangchuan; Neeracher, Matthias; Prahallad, Kishore (20 de agosto de 2017). «Siri On-Device Deep Learning-Guided Unit Selection Text-to-Speech System». ISCA. Interspeech 2017 (em inglês): 4011–4015. doi:10.21437/Interspeech.2017-1798

jku.at

bioinf.jku.at

Hochreiter, Sepp (1991). Untersuchungen zu dynamischen neuronalen Netzen (PDF) (Tese de diploma). Technical University Munich, Institute of Computer Science
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). «Learning to Learn Using Gradient Descent». Artificial Neural Networks — ICANN 2001 (PDF). Col: Lecture Notes in Computer Science. 2130. [S.l.: s.n.] pp. 87–94. CiteSeerX 10.1.1.5.323. ISBN 978-3-540-42486-4. ISSN 0302-9743. doi:10.1007/3-540-44668-0_13

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

kdd.org

«Patient Subtyping via Time-Aware LSTM Networks». Kdd.org. Consultado em 24 de maio de 2018
«SIGKDD». Kdd.org. Consultado em 24 de maio de 2018

medium.com

Stanford, Stacy (25 de janeiro de 2019). «IA da DeepMind, AlphaStar, Mostra Progresso Significativo em Direção à Inteligência Artificial Geral». Medium ML Memoirs. Consultado em 15 de janeiro de 2019

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». JMLR Workshop and Conference Proceedings. 56: 301–318. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600. arXiv:1511.05942

msu.edu

biometrics.cse.msu.edu

«Patient Subtyping via Time-Aware LSTM Networks» (PDF). msu.edu. Consultado em 21 de novembro de 2018

neurips.cc

Hochreiter, Sepp; Schmidhuber, Juergen (1996). LSTM can solve hard long time lag problems. Advances in Neural Information Processing Systems

newatlas.com

Haridy, Rich (21 de agosto de 2017). «O sistema de reconhecimento de fala da Microsoft agora é tão bom quanto o de um humano». newatlas.com. Consultado em 27 de agosto de 2017

nih.gov

ncbi.nlm.nih.gov

Sepp Hochreiter; Jürgen Schmidhuber (1997). «Long short-term memory». Neural Computation. 9 (8): 1735–1780. PMID 9377276. doi:10.1162/neco.1997.9.8.1735
Graves, A.; Liwicki, M.; Fernández, S.; Bertolami, R.; Bunke, H.; Schmidhuber, J. (maio de 2009). «Um Novo Sistema Conexista para Reconhecimento de Escrita à Mão Sem Restrições». IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5): 855–868. CiteSeerX 10.1.1.139.4502. ISSN 0162-8828. PMID 19299860. doi:10.1109/tpami.2008.137
Felix A. Gers; Jürgen Schmidhuber; Fred Cummins (2000). «Learning to Forget: Continual Prediction with LSTM». Neural Computation. 12 (10): 2451–2471. CiteSeerX 10.1.1.55.5709. PMID 11032042. doi:10.1162/089976600300015015
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. PMID 18249962. doi:10.1109/72.963769
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. PMID 16112549. doi:10.1016/j.neunet.2005.06.042
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. PMID 12184841. doi:10.1162/089976602320263980
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. PMID 12628609. 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. PMID 17488755. doi:10.1093/bioinformatics/btm247
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. PMID 17666763. doi:10.1109/tcbb.2007.1015
Choi, E.; Bahadori, M.T.; Schuetz, E.; Stewart, W.; Sun, J. (2016). «Doctor AI: Predicting Clinical Events via Recurrent Neural Networks». JMLR Workshop and Conference Proceedings. 56: 301–318. Bibcode:2015arXiv151105942C. PMC 5341604. PMID 28286600. arXiv:1511.05942
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (22 de maio de 2018). «Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation» (PDF). Sensors. 18 (5): 1657. Bibcode:2018Senso..18.1657W. ISSN 1424-8220. PMC 5982167. PMID 29789447. doi:10.3390/s18051657
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). PMC 5836943. PMID 29095571. doi:10.1002/minf.201700111
Klaus Greff; Rupesh Kumar Srivastava; Jan Koutník; Bas R. Steunebrink; Jürgen Schmidhuber (2015). «LSTM: A Search Space Odyssey». IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. Bibcode:2015arXiv150304069G. PMID 27411231. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924

nips.cc

papers.nips.cc

Srivastava, Rupesh K; Greff, Klaus; Schmidhuber, Juergen (2015). «Treinamento de Redes Muito Profundas». Curran Associates, Inc. Avanços em Sistemas de Informação Neural. 28: 2377–2385

openai.com

«Aprendizado de Destreza». OpenAI. 30 de julho de 2018. Consultado em 28 de junho de 2023

psu.edu

citeseerx.ist.psu.edu

Graves, A.; Liwicki, M.; Fernández, S.; Bertolami, R.; Bunke, H.; Schmidhuber, J. (maio de 2009). «Um Novo Sistema Conexista para Reconhecimento de Escrita à Mão Sem Restrições». IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5): 855–868. CiteSeerX 10.1.1.139.4502. ISSN 0162-8828. PMID 19299860. doi:10.1109/tpami.2008.137
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (Outubro de 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. [S.l.: s.n.] pp. 543–548. CiteSeerX 10.1.1.218.3399. ISBN 978-1-4244-0258-8. doi:10.1109/IROS.2006.282190
Felix A. Gers; Jürgen Schmidhuber; Fred Cummins (2000). «Learning to Forget: Continual Prediction with LSTM». Neural Computation. 12 (10): 2451–2471. CiteSeerX 10.1.1.55.5709. PMID 11032042. doi:10.1162/089976600300015015
Fernández, Santiago; Graves, Alex; Schmidhuber, Jürgen (2007). «Rotulagem de sequências em domínios estruturados com redes neurais recorrentes hierárquicas». Proc. 20th Int. Joint Conf. On Artificial Intelligence, Ijcai 2007: 774–779. CiteSeerX 10.1.1.79.1887
Graves, Alex; Fernández, Santiago; Gomez, Faustino; Schmidhuber, Jürgen (2006). «Classificação temporal conexa: Rotulagem de dados de sequência não segmentada com redes neurais recorrentes». Em Atas da Conferência Internacional sobre Aprendizado de Máquina, ICML 2006: 369–376. CiteSeerX 10.1.1.75.6306
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. PMID 16112549. doi:10.1016/j.neunet.2005.06.042
Eck, Douglas; Schmidhuber, Jürgen (28 de agosto de 2002). «Learning the Long-Term Structure of the Blues». Artificial Neural Networks — ICANN 2002. Col: Lecture Notes in Computer Science. 2415. [S.l.]: Springer, Berlin, Heidelberg. pp. 284–289. CiteSeerX 10.1.1.116.3620. ISBN 978-3540460848. doi:10.1007/3-540-46084-5_47
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. PMID 12184841. doi:10.1162/089976602320263980
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. PMID 12628609. doi:10.1016/s0893-6080(02)00219-8
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). «Learning to Learn Using Gradient Descent». Artificial Neural Networks — ICANN 2001 (PDF). Col: Lecture Notes in Computer Science. 2130. [S.l.: s.n.] pp. 87–94. CiteSeerX 10.1.1.5.323. ISBN 978-3-540-42486-4. ISSN 0302-9743. doi:10.1007/3-540-44668-0_13

qilin-zhang.github.io

Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (22 de maio de 2018). «Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation» (PDF). Sensors. 18 (5): 1657. Bibcode:2018Senso..18.1657W. ISSN 1424-8220. PMC 5982167. PMID 29789447. doi:10.3390/s18051657

researchgate.net

Sepp Hochreiter; Jürgen Schmidhuber (1997). «Long short-term memory». Neural Computation. 9 (8): 1735–1780. PMID 9377276. doi:10.1162/neco.1997.9.8.1735
Hochreiter, S.; Bengio, Y.; Frasconi, P.; Schmidhuber, J. (2001). «Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies (PDF Download Available)». In: Kremer and, S. C.; Kolen, J. F. A Field Guide to Dynamical Recurrent Neural Networks. [S.l.]: IEEE Press

theinformation.com

Efrati, Amir (13 de junho de 2016). «Apple's Machines Can Learn Too». The Information. Consultado em 27 de junho de 2017

theverge.com

Ong, Thuy (4 de agosto de 2017). «Facebook's translations are now powered completely by AI». www.allthingsdistributed.com. Consultado em 15 de fevereiro de 2019

towardsdatascience.com

Rodriguez, Jesus (2 de julho de 2018). «A Ciência por Trás do OpenAI Five que Acaba de Produzir um dos Maiores Avanços na História da IA». Towards Data Science. Consultado em 15 de janeiro de 2019. Cópia arquivada em 26 de dezembro de 2019

ucl.ac.be

elen.ucl.ac.be

Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Agarwal, Puneet (Abril de 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. Consultado em 21 de fevereiro de 2018. Cópia arquivada (PDF) em 30 de outubro de 2020

uva.nl

pure.uva.nl

Lakretz, Yair; Kruszewski, German; Desbordes, Theo; Hupkes, Dieuwke; Dehaene, Stanislas; Baroni, Marco (2019), «The emergence of number and syntax units in», The emergence of number and syntax units (PDF), Association for Computational Linguistics, pp. 11–20, doi:10.18653/v1/N19-1002, hdl:11245.1/16cb6800-e10d-4166-8e0b-fed61ca6ebb4

web.archive.org

Sak, Hasim; Senior, Andrew; Beaufays, Francoise (2014). «Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling» (PDF). Cópia arquivada (PDF) em 24 de abril de 2018
Rodriguez, Jesus (2 de julho de 2018). «A Ciência por Trás do OpenAI Five que Acaba de Produzir um dos Maiores Avanços na História da IA». Towards Data Science. Consultado em 15 de janeiro de 2019. Cópia arquivada em 26 de dezembro de 2019
Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Agarwal, Puneet (Abril de 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. Consultado em 21 de fevereiro de 2018. Cópia arquivada (PDF) em 30 de outubro de 2020

wikidata.org

Sepp Hochreiter; Jürgen Schmidhuber (21 de agosto de 1995), Long Short Term Memory, Wikidata Q98967430

wired.com

Metz, Cade (27 de setembro de 2016). «An Infusion of AI Makes Google Translate More Powerful Than Ever | WIRED». Wired. Consultado em 27 de junho de 2017

worldcat.org

Graves, A.; Liwicki, M.; Fernández, S.; Bertolami, R.; Bunke, H.; Schmidhuber, J. (maio de 2009). «Um Novo Sistema Conexista para Reconhecimento de Escrita à Mão Sem Restrições». IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5): 855–868. CiteSeerX 10.1.1.139.4502. ISSN 0162-8828. PMID 19299860. doi:10.1109/tpami.2008.137
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (22 de maio de 2018). «Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation» (PDF). Sensors. 18 (5): 1657. Bibcode:2018Senso..18.1657W. ISSN 1424-8220. PMC 5982167. PMID 29789447. doi:10.3390/s18051657
Saiful Islam, Md.; Hossain, Emam (26 de outubro de 2020). «Foreign Exchange Currency Rate Prediction using a GRU-LSTM Hybrid Network». Soft Computing Letters (em inglês). 3. 100009 páginas. ISSN 2666-2221. doi:10.1016/j.socl.2020.100009
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). «Learning to Learn Using Gradient Descent». Artificial Neural Networks — ICANN 2001 (PDF). Col: Lecture Notes in Computer Science. 2130. [S.l.: s.n.] pp. 87–94. CiteSeerX 10.1.1.5.323. ISBN 978-3-540-42486-4. ISSN 0302-9743. doi:10.1007/3-540-44668-0_13

zdnet.com

Ranger, Steve (14 de junho de 2016). «iPhone, AI and big data: Here's how Apple plans to protect your privacy | ZDNet». ZDNet. Consultado em 27 de junho de 2017