回帰型ニューラルネットワーク (Japanese Wikipedia)

Analysis of information sources in references of the Wikipedia article "回帰型ニューラルネットワーク" in Japanese language version.

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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. https://www.academia.edu/5830256.

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.
Gomez, Faustino; Schmidhuber, Jürgen; Miikkulainen, Risto (June 2008). “Accelerated Neural Evolution Through Cooperatively Coevolved Synapses”. J. Mach. Learn. Res. 9: 937–965.
Fernández, Santiago; Graves, Alex; Schmidhuber, Jürgen (2007). An Application of Recurrent Neural Networks to Discriminative Keyword Spotting. ICANN'07. Berlin, Heidelberg: Springer-Verlag. 220–229. ISBN 978-3540746935
Graves, Alex; Fernández, Santiago; Liwicki, Marcus; Bunke, Horst; Schmidhuber, Jürgen (2007). Unconstrained Online Handwriting Recognition with Recurrent Neural Networks. NIPS'07. USA: Curran Associates Inc.. 577–584. ISBN 9781605603520

arimaa.com

“Applying Genetic Algorithms to Recurrent Neural Networks for Learning Network Parameters and Architecture”. 2019年4月5日閲覧。

arxiv.org

Li, Xiangang; Wu, Xihong (15 October 2014). "Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition". arXiv:1410.4281 [cs.CL]。
Schmidhuber, Jürgen (January 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.
Hannun, Awni; Case, Carl; Casper, Jared; Catanzaro, Bryan; Diamos, Greg; Elsen, Erich; Prenger, Ryan; Satheesh, Sanjeev; Sengupta, Shubho (17 December 2014). "Deep Speech: Scaling up end-to-end speech recognition". arXiv:1412.5567 [cs.CL]。
Sutskever, L.; Vinyals, O.; Le, Q. (2014). “Sequence to Sequence Learning with Neural Networks”. Electronic Proceedings of the Neural Information Processing Systems Conference 27: 5346. arXiv:1409.3215. Bibcode: 2014arXiv1409.3215S.
Jozefowicz, Rafal; Vinyals, Oriol; Schuster, Mike; Shazeer, Noam; Wu, Yonghui (7 February 2016). "Exploring the Limits of Language Modeling". arXiv:1602.02410 [cs.CL]。
Gillick, Dan; Brunk, Cliff; Vinyals, Oriol; Subramanya, Amarnag (30 November 2015). "Multilingual Language Processing From Bytes". arXiv:1512.00103 [cs.CL]。
Vinyals, Oriol; Toshev, Alexander; Bengio, Samy; Erhan, Dumitru (17 November 2014). "Show and Tell: A Neural Image Caption Generator". arXiv:1411.4555 [cs.CV]。
Li, Shuai; Li, Wanqing; Cook, Chris; Zhu, Ce; Yanbo, Gao (2018). “Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN”. IEEE Conference on Computer Vision and Pattern Recognition. arXiv:1803.04831.
Heck, Joel; Salem, Fathi M. (12 January 2017). "Simplified Minimal Gated Unit Variations for Recurrent Neural Networks". arXiv:1701.03452 [cs.NE]。
Dey, Rahul; Salem, Fathi M. (20 January 2017). "Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks". arXiv:1701.05923 [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]。
Graves, Alex; Wayne, Greg; Danihelka, Ivo (2014). "Neural Turing Machines". arXiv:1410.5401 [cs.NE]。
Yann, Ollivier; Corentin, Tallec; Guillaume, Charpiat (28 July 2015). "Training recurrent networks online without backtracking". arXiv:1507.07680 [cs.NE]。
"Copying task. This standard RNN task ... directly tests memorization, where models must regurgitate a sequence of tokens seen at the beginning of the sequence." Gu, et al. (2020). HiPPO: Recurrent Memory with Optimal Polynomial Projections.
" the first 10 tokens (a0, a1, . . . , a9) are randomly chosen from {1, . . . , 8}, the middle N tokens are set to 0, and the last ten tokens are 9. The goal of the recurrent model is to output (a0, . . . , a9) in order on the last 10 time steps, whenever the cue token 9 is presented." Gu, et al. (2020). HiPPO: Recurrent Memory with Optimal Polynomial Projections.
"ability to recall exactly data seen a long time ago." Arjovsky, et al. (2015). Unitary Evolution Recurrent Neural Networks.
Figure 1 of Arjovsky, et al. (2015). Unitary Evolution Recurrent Neural Networks.
Figure 7 of Gu, et al. (2020). HiPPO: Recurrent Memory with Optimal Polynomial Projections.
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). Predictive Business Process Monitoring with LSTM neural networks. 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

brains-minds-media.org

Cruse, Holk; Neural Networks as Cybernetic Systems, 2nd and revised edition

doi.org

Williams, Ronald J.; Hinton, Geoffrey E.; Rumelhart, David E. (October 1986). “Learning representations by back-propagating errors”. Nature 323 (6088): 533–536. doi:10.1038/323533a0. ISSN 1476-4687.
Schmidhuber, Jürgen (January 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.
Elman, Jeffrey L. (1990). “Finding Structure in Time”. Cognitive Science 14 (2): 179–211. doi:10.1016/0364-0213(90)90002-E.
Jordan, Michael I. (1997-01-01). Serial Order: A Parallel Distributed Processing Approach. Neural-Network Models of Cognition. 121. 471–495. doi:10.1016/s0166-4115(97)80111-2. ISBN 9780444819314
Kosko, B. (1988). “Bidirectional associative memories”. IEEE Transactions on Systems, Man, and Cybernetics 18 (1): 49–60. doi:10.1109/21.87054.
Rakkiyappan, R.; Chandrasekar, A.; Lakshmanan, S.; Park, Ju H. (2 January 2015). “Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control”. Complexity 20 (3): 39–65. Bibcode: 2015Cmplx..20c..39R. doi:10.1002/cplx.21503.
Jaeger, Herbert; Haas, Harald (2004-04-02). “Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication”. Science 304 (5667): 78–80. Bibcode: 2004Sci...304...78J. doi:10.1126/science.1091277. PMID 15064413.
Goller, C.; Küchler, A. (1996). Learning task-dependent distributed representations by backpropagation through structure. 1. 347. doi:10.1109/ICNN.1996.548916. ISBN 978-0-7803-3210-2
Schmidhuber, Jürgen (1992). “Learning complex, extended sequences using the principle of history compression”. Neural Computation 4 (2): 234–242. doi:10.1162/neco.1992.4.2.234.
Schmidhuber, Jürgen (2015). “Deep Learning”. Scholarpedia 10 (11): 32832. Bibcode: 2015SchpJ..1032832S. doi:10.4249/scholarpedia.32832.
Gers, Felix; Schraudolph, Nicol N.; Schmidhuber, Jürgen (2000). “Learning Precise Timing with LSTM Recurrent Networks (PDF Download Available)”. Crossref Listing of Deleted Dois 1. doi:10.1162/153244303768966139 2019年4月5日閲覧。.
Bayer, Justin; Wierstra, Daan; Togelius, Julian; Schmidhuber, Jürgen (2009-09-14). Evolving Memory Cell Structures for Sequence Learning. Lecture Notes in Computer Science. 5769. Springer, Berlin, Heidelberg. 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. PMID 18249962.
Graves, Alex; Schmidhuber, Jürgen (2005-07-01). “Framewise phoneme classification with bidirectional LSTM and other neural network architectures”. Neural Networks. IJCNN 2005 18 (5): 602–610. doi:10.1016/j.neunet.2005.06.042. PMID 16112549.
Thireou, T.; Reczko, M. (July 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.
Quinn, Matthew (2001). Evolving communication without dedicated communication channels. Lecture Notes in Computer Science. 2159. 357–366. doi:10.1007/3-540-44811-X_38. ISBN 978-3-540-42567-0
Beer, R.D. (1997). “The dynamics of adaptive behavior: A research program”. Robotics and Autonomous Systems 20 (2–4): 257–289. doi:10.1016/S0921-8890(96)00063-2.
Paine, Rainer W.; Tani, Jun (2005-09-01). “How Hierarchical Control Self-organizes in Artificial Adaptive Systems”. Adaptive Behavior 13 (3): 211–225. doi:10.1177/105971230501300303.
Yamashita, Yuichi; Tani, Jun (2008-11-07). “Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment”. PLOS Computational Biology 4 (11): e1000220. Bibcode: 2008PLSCB...4E0220Y. doi:10.1371/journal.pcbi.1000220. PMC 2570613. PMID 18989398.
Shibata Alnajjar, Fady; Yamashita, Yuichi; Tani, Jun (2013). “The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory”. Frontiers in Neurorobotics 7: 2. doi:10.3389/fnbot.2013.00002. PMC 3575058. PMID 23423881.
Sun, Guo-Zheng; Giles, C. Lee; Chen, Hsing-Hen (1998). “The Neural Network Pushdown Automaton: Architecture, Dynamics and Training”. In Giles, C. Lee. Adaptive Processing of Sequences and Data Structures. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 296–345. doi:10.1007/bfb0054003. ISBN 9783540643418
Werbos, Paul J. (1988). “Generalization of backpropagation with application to a recurrent gas market model”. Neural Networks 1 (4): 339–356. doi:10.1016/0893-6080(88)90007-x.
SCHMIDHUBER, JURGEN (1989-01-01). “A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks”. Connection Science 1 (4): 403–412. doi:10.1080/09540098908915650.
Schmidhuber, Jürgen (1992-03-01). “A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks”. Neural Computation 4 (2): 243–248. doi:10.1162/neco.1992.4.2.243.
Pearlmutter, Barak A. (1989-06-01). “Learning State Space Trajectories in Recurrent Neural Networks”. Neural Computation 1 (2): 263–269. doi:10.1162/neco.1989.1.2.263.
Hochreiter, Sepp; Schmidhuber, Jürgen (1997-11-01). “Long Short-Term Memory”. Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
Campolucci; Uncini, A.; Piazza, F.; Rao, B. D. (1999). “On-Line Learning Algorithms for Locally Recurrent Neural Networks”. IEEE Transactions on Neural Networks 10 (2): 253–271. doi:10.1109/72.750549. PMID 18252525.
Wan, E. A.; Beaufays, F. (1996). “Diagrammatic derivation of gradient algorithms for neural networks”. Neural Computation 8: 182–201. doi:10.1162/neco.1996.8.1.182.
Campolucci, P.; Uncini, A.; Piazza, F. (2000). “A Signal-Flow-Graph Approach to On-line Gradient Calculation”. Neural Computation 12 (8): 1901–1927. doi:10.1162/089976600300015196.
Mayer, H.; Gomez, F.; Wierstra, D.; Nagy, I.; Knoll, A.; Schmidhuber, J. (October 2006). A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. 543–548. 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. doi:10.1016/j.neunet.2005.06.042. PMID 16112549.
Eck, Douglas; Schmidhuber, Jürgen (2002-08-28). Learning the Long-Term Structure of the Blues. Lecture Notes in Computer Science. 2415. Springer, Berlin, Heidelberg. 284–289. 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. doi:10.1162/089976602320263980. PMID 12184841.
Gers, F. A.; Schmidhuber, J. (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. PMID 18249962.
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. PMID 17666763.
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M. (2017). Predictive Business Process Monitoring with LSTM neural networks. 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

elsevier.com

linkinghub.elsevier.com

Werbos, Paul J. (1988). “Generalization of backpropagation with application to a recurrent gas market model”. Neural Networks 1 (4): 339–356. doi:10.1016/0893-6080(88)90007-x.

google.co.jp

books.google.co.jp

Rául Rojas (1996). Neural networks: a systematic introduction. Springer. p. 336. ISBN 978-3-540-60505-8
Griewank, Andreas; Walther, Andrea (2008). Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Second ed.). SIAM. ISBN 978-0-89871-776-1
Rumelhart, David E. (1985). Learning Internal Representations by Error Propagation. Institute for Cognitive Science, University of California, San Diego
Robinson, A. J. (1987). The Utility Driven Dynamic Error Propagation Network. Technical Report CUED/F-INFENG/TR.1. University of Cambridge Department of Engineering
Williams, R. J.; Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity, D. (1 February 2013). Backpropagation: Theory, Architectures, and Applications. Psychology Press. ISBN 978-1-134-77581-1
Príncipe, José C.; Euliano, Neil R.; Lefebvre, W. Curt (2000). Neural and adaptive systems: fundamentals through simulations. Wiley. ISBN 978-0-471-35167-2
Hochreiter, S. (15 January 2001). “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies”. A Field Guide to Dynamical Recurrent Networks. John Wiley & Sons. ISBN 978-0-7803-5369-5
Siegelmann, Hava T.; Horne, Bill G.; Giles, C. Lee (1995). Computational Capabilities of Recurrent NARX Neural Networks. University of Maryland

googleresearch.blogspot.ch

Sak, Haşim (September 2015). “Google voice search: faster and more accurate”. 2019年4月5日閲覧。

googleusercontent.com

static.googleusercontent.com

Sak, Hasim (2014年). “Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling”. 2019年4月5日閲覧。
Zen, Heiga (2015年). “Unidirectional Long Short-Term Memory Recurrent Neural Network with Recurrent Output Layer for Low-Latency Speech Synthesis”. Google.com. ICASSP. pp. 4470–4474. 2019年4月5日閲覧。

harvard.edu

ui.adsabs.harvard.edu

Sutskever, L.; Vinyals, O.; Le, Q. (2014). “Sequence to Sequence Learning with Neural Networks”. Electronic Proceedings of the Neural Information Processing Systems Conference 27: 5346. arXiv:1409.3215. Bibcode: 2014arXiv1409.3215S.
Rakkiyappan, R.; Chandrasekar, A.; Lakshmanan, S.; Park, Ju H. (2 January 2015). “Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control”. Complexity 20 (3): 39–65. Bibcode: 2015Cmplx..20c..39R. doi:10.1002/cplx.21503.
Jaeger, Herbert; Haas, Harald (2004-04-02). “Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication”. Science 304 (5667): 78–80. Bibcode: 2004Sci...304...78J. doi:10.1126/science.1091277. PMID 15064413.
Schmidhuber, Jürgen (2015). “Deep Learning”. Scholarpedia 10 (11): 32832. Bibcode: 2015SchpJ..1032832S. doi:10.4249/scholarpedia.32832.
Yamashita, Yuichi; Tani, Jun (2008-11-07). “Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment”. PLOS Computational Biology 4 (11): e1000220. Bibcode: 2008PLSCB...4E0220Y. doi:10.1371/journal.pcbi.1000220. PMC 2570613. PMID 18989398.

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).

people.idsia.ch

Sepp Hochreiter (1991), Untersuchungen zu dynamischen neuronalen Netzen, Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber.