Richness of Deep Echo State Network Dynamics

Principe, J.C., Xu, D., Fisher, J.: Information theoretic learning. Unsupervised adaptive filtering 1, 265–319 (2000)

2000

Earlier work this paper cites.

Jaeger, H.: The ”echo state” approach to analysing and training recurrent neural networks - with an erratum note. Tech. rep., GMD - German National Research Institute for Computer Science (2001)

2001

Earlier work this paper cites.

Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)

2004

Earlier work this paper cites.

Jaeger, H.: Reservoir riddles: Suggestions for echo state network research. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks (IJCNN). vol. 3, pp. 1460–1462. IEEE (2005)

2005

Earlier work this paper cites.

Ozturk, M.C., Xu, D., Príncipe, J.C.: Analysis and design of echo state networks. Neural computation 19(1), 111–138 (2007)

2007

Earlier work this paper cites.

Tiňo, P., Hammer, B., Bodén, M.: Markovian bias of neural-based architectures with feedback connections. In: Perspectives of neural-symbolic integration, pp. 95–133. Springer (2007)

2007

Earlier work this paper cites.

Verstraeten, D., Schrauwen, B., d’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural networks 20(3), 391–403 (2007)

2007

Earlier work this paper cites.

Xue, Y., Yang, L., Haykin, S.: Decoupled echo state networks with lateral inhibition. Neural Networks 20(3), 365–376 (2007)

2007

Earlier work this paper cites.

Haykin, S.: Neural networks and learning machines. Pearson, 3 edn. (2009)

2009

Earlier work this paper cites.

Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)

2009

Earlier work this paper cites.

Gallicchio, C., Micheli, A.: A markovian characterization of redundancy in echo state networks by pca. In: Proc. of the 18th European Symposium on Artificial Neural Networks (ESANN). d-side publi. (2010)

2010

Cited alongside, same era.

Principe, J.C.: Information theoretic learning: Renyi’s entropy and kernel perspectives. Springer Science & Business Media (2010)

2010

Cited alongside, same era.

Gallicchio, C., Micheli, A.: Architectural and markovian factors of echo state networks. Neural Networks 24(5), 440–456 (2011)

2011

Cited alongside, same era.

Boedecker, J., Obst, O., Lizier, J.T., Mayer, N.M., Asada, M.: Information processing in echo state networks at the edge of chaos. Theory in Biosciences 131(3), 205–213 (2012)

2012

Cited alongside, same era.

Yildiz, I., Jaeger, H., Kiebel, S.: Re-visiting the echo state property. Neural networks 35, 1–9 (2012)

Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: A critical experimental analysis. Neurocomputing 268, 87–99 (2017)

2017

Later among the works it cites.

Gallicchio, C., Micheli, A.: Deep echo state network (deepesn): A brief survey. arXiv preprint arXiv:1712.04323 (2017)

Original

2017

Later among the works it cites.

Scardapane, S., Wang, D.: Randomness in neural networks: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7(2), e1200 (2017)

2017

Later among the works it cites.

Gallicchio, C., Micheli, A., Tiňo, P.: Randomized recurrent neural networks. In: 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). pp. 415–424. i6doc. com publication (2018)

2018

Later among the works it cites.